release
This commit is contained in:
allenzren
@@ -0,0 +1,119 @@
|
||||
defaults:
|
||||
- _self_
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||||
hydra:
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||||
run:
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dir: ${logdir}
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||||
_target_: agent.finetune.train_ppo_diffusion_agent.TrainPPODiffusionAgent
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||||
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name: ${env_name}_m1_ft_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}_tdf${ft_denoising_steps}
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||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
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||||
base_policy_path: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/m1/avoid_d56_r12_pre_diffusion_mlp_ta4_td20/2024-07-06_22-50-07/checkpoint/state_10000.pt
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normalization_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m1/normalization.npz
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||||
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||||
seed: 42
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||||
device: cuda:0
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env_name: avoiding-m5
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mode: d56_r12 # M1, desired modes 5 and 6, required modes 1 and 2
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obs_dim: 4
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action_dim: 2
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transition_dim: ${action_dim}
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||||
denoising_steps: 20
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ft_denoising_steps: 10
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||||
cond_steps: 1
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||||
horizon_steps: 4
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||||
act_steps: 4
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||||
|
||||
env:
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||||
n_envs: 50
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||||
name: ${env_name}
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||||
max_episode_steps: 100
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||||
reset_at_iteration: True
|
||||
save_video: False
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||||
best_reward_threshold_for_success: 2
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||||
save_full_observations: True
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||||
wrappers:
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d3il_lowdim:
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||||
normalization_path: ${normalization_path}
|
||||
multi_step:
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||||
n_obs_steps: ${cond_steps}
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||||
n_action_steps: ${act_steps}
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||||
max_episode_steps: ${env.max_episode_steps}
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||||
pass_full_observations: ${env.save_full_observations}
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||||
reset_within_step: False
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||||
|
||||
wandb:
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entity: ${oc.env:DPPO_WANDB_ENTITY}
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project: d3il-${env_name}-m1-finetune
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run: ${now:%H-%M-%S}_${name}
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||||
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||||
train:
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n_train_itr: 51
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||||
n_critic_warmup_itr: 1
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n_steps: ${eval:'round(${env.max_episode_steps} / ${act_steps})'}
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||||
gamma: 0.99
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||||
actor_lr: 1e-5
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||||
actor_weight_decay: 0
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||||
actor_lr_scheduler:
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||||
first_cycle_steps: 100
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||||
warmup_steps: 10
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min_lr: 1e-5
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||||
critic_lr: 1e-3
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||||
critic_weight_decay: 0
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||||
critic_lr_scheduler:
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first_cycle_steps: 100
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||||
warmup_steps: 10
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||||
min_lr: 1e-3
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||||
save_model_freq: 100
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val_freq: 100 # no eval, always train mode
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force_train: True
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||||
render:
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freq: 1
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||||
num: 10
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||||
plotter:
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_target_: env.plot_traj.TrajPlotter
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||||
env_type: avoid
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normalization_path: ${normalization_path}
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||||
# PPO specific
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||||
reward_scale_running: True
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||||
reward_scale_const: 1.0
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gae_lambda: 0.95
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batch_size: ${eval:'round(${train.n_steps} * ${env.n_envs} / 2)'}
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update_epochs: 10
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vf_coef: 0.5
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target_kl: 1
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||||
model:
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_target_: model.diffusion.diffusion_ppo.PPODiffusion
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||||
# HP to tune
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gamma_denoising: 0.95
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||||
clip_ploss_coef: 0.1
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clip_ploss_coef_base: 0.1
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clip_ploss_coef_rate: 1
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randn_clip_value: 3
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min_sampling_denoising_std: 0.1
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min_logprob_denoising_std: 0.1
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#
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network_path: ${base_policy_path}
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actor:
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_target_: model.diffusion.mlp_diffusion.DiffusionMLP
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time_dim: 16
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mlp_dims: [512, 512, 512]
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activation_type: ReLU
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residual_style: True
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||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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horizon_steps: ${horizon_steps}
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transition_dim: ${transition_dim}
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critic:
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_target_: model.common.critic.CriticObs
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||||
obs_dim: ${obs_dim}
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mlp_dims: [256, 256, 256]
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activation_type: Mish
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||||
residual_style: True
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||||
ft_denoising_steps: ${ft_denoising_steps}
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||||
transition_dim: ${transition_dim}
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||||
horizon_steps: ${horizon_steps}
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||||
obs_dim: ${obs_dim}
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||||
action_dim: ${action_dim}
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cond_steps: ${cond_steps}
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||||
denoising_steps: ${denoising_steps}
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||||
device: ${device}
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||||
@@ -0,0 +1,105 @@
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||||
defaults:
|
||||
- _self_
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||||
hydra:
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||||
run:
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||||
dir: ${logdir}
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||||
_target_: agent.finetune.train_ppo_gaussian_agent.TrainPPOGaussianAgent
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||||
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||||
name: ${env_name}_m1_gaussian_mlp_ta${horizon_steps}
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||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
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||||
base_policy_path: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/m1/avoid_d56_r12_pre_gaussian_mlp_ta4/2024-07-07_01-35-48/checkpoint/state_10000.pt
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normalization_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m1/normalization.npz
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||||
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||||
seed: 42
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||||
device: cuda:0
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||||
env_name: avoiding-m5
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||||
mode: d56_r12 # M1, desired modes 5 and 6, required modes 1 and 2
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obs_dim: 4
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||||
action_dim: 2
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||||
transition_dim: ${action_dim}
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||||
cond_steps: 1
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||||
horizon_steps: 4
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||||
act_steps: 4
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||||
|
||||
env:
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||||
n_envs: 50
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||||
name: ${env_name}
|
||||
max_episode_steps: 100
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||||
reset_at_iteration: True
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||||
save_video: False
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||||
best_reward_threshold_for_success: 2
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||||
save_full_observations: True
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||||
wrappers:
|
||||
d3il_lowdim:
|
||||
normalization_path: ${normalization_path}
|
||||
multi_step:
|
||||
n_obs_steps: ${cond_steps}
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||||
n_action_steps: ${act_steps}
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||||
max_episode_steps: ${env.max_episode_steps}
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||||
pass_full_observations: ${env.save_full_observations}
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||||
reset_within_step: False
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||||
|
||||
wandb:
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||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
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||||
project: d3il-${env_name}-m1-finetune
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||||
run: ${now:%H-%M-%S}_${name}
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||||
|
||||
train:
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||||
n_train_itr: 51
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||||
n_critic_warmup_itr: 1
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||||
n_steps: ${eval:'round(${env.max_episode_steps} / ${act_steps})'}
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||||
gamma: 0.99
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||||
actor_lr: 1e-5
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||||
actor_weight_decay: 0
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||||
actor_lr_scheduler:
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||||
first_cycle_steps: 100
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||||
warmup_steps: 10
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||||
min_lr: 1e-5
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critic_lr: 1e-3
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||||
critic_weight_decay: 0
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||||
critic_lr_scheduler:
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||||
first_cycle_steps: 100
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||||
warmup_steps: 10
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||||
min_lr: 1e-3
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||||
save_model_freq: 100
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||||
val_freq: 100 # no eval, always train mode
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force_train: True
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||||
render:
|
||||
freq: 1
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||||
num: 10
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||||
plotter:
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||||
_target_: env.plot_traj.TrajPlotter
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||||
env_type: avoid
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||||
normalization_path: ${normalization_path}
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||||
# PPO specific
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||||
reward_scale_running: True
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||||
reward_scale_const: 1.0
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||||
gae_lambda: 0.95
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||||
batch_size: ${eval:'round(${train.n_steps} * ${env.n_envs} / 2)'}
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||||
update_epochs: 10
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||||
vf_coef: 0.5
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||||
target_kl: 1
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||||
|
||||
model:
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||||
_target_: model.rl.gaussian_ppo.PPO_Gaussian
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||||
clip_ploss_coef: 0.1
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||||
randn_clip_value: 3
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||||
network_path: ${base_policy_path}
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||||
actor:
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_target_: model.common.mlp_gaussian.Gaussian_MLP
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mlp_dims: [256, 256, 256] # smaller MLP for less overfitting
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||||
activation_type: ReLU
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||||
residual_style: True
|
||||
fixed_std: 0.1
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learn_fixed_std: False
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||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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||||
horizon_steps: ${horizon_steps}
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||||
transition_dim: ${transition_dim}
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||||
critic:
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||||
_target_: model.common.critic.CriticObs
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mlp_dims: [256, 256, 256]
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residual_style: True
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||||
obs_dim: ${obs_dim}
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||||
horizon_steps: ${horizon_steps}
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||||
cond_steps: ${cond_steps}
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||||
device: ${device}
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||||
@@ -0,0 +1,106 @@
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||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
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||||
dir: ${logdir}
|
||||
_target_: agent.finetune.train_ppo_gaussian_agent.TrainPPOGaussianAgent
|
||||
|
||||
name: ${env_name}_m1_gmm_mlp_ta${horizon_steps}
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||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
base_policy_path: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/m1/avoid_d56_r12_pre_gmm_mlp_ta4/2024-07-10_14-30-00/checkpoint/state_10000.pt
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normalization_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m1/normalization.npz
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||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env_name: avoiding-m5
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||||
mode: d56_r12 # M1, desired modes 5 and 6, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
cond_steps: 1
|
||||
horizon_steps: 4
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||||
act_steps: 4
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||||
num_modes: 5
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||||
|
||||
env:
|
||||
n_envs: 50
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||||
name: ${env_name}
|
||||
max_episode_steps: 100
|
||||
reset_at_iteration: True
|
||||
save_video: False
|
||||
best_reward_threshold_for_success: 2
|
||||
save_full_observations: True
|
||||
wrappers:
|
||||
d3il_lowdim:
|
||||
normalization_path: ${normalization_path}
|
||||
multi_step:
|
||||
n_obs_steps: ${cond_steps}
|
||||
n_action_steps: ${act_steps}
|
||||
max_episode_steps: ${env.max_episode_steps}
|
||||
pass_full_observations: ${env.save_full_observations}
|
||||
reset_within_step: False
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env_name}-m1-finetune
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_train_itr: 51
|
||||
n_critic_warmup_itr: 1
|
||||
n_steps: ${eval:'round(${env.max_episode_steps} / ${act_steps})'}
|
||||
gamma: 0.99
|
||||
actor_lr: 1e-5
|
||||
actor_weight_decay: 0
|
||||
actor_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-5
|
||||
critic_lr: 1e-3
|
||||
critic_weight_decay: 0
|
||||
critic_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-3
|
||||
save_model_freq: 100
|
||||
val_freq: 100 # no eval, always train mode
|
||||
force_train: True
|
||||
render:
|
||||
freq: 1
|
||||
num: 10
|
||||
plotter:
|
||||
_target_: env.plot_traj.TrajPlotter
|
||||
env_type: avoid
|
||||
normalization_path: ${normalization_path}
|
||||
# PPO specific
|
||||
reward_scale_running: True
|
||||
reward_scale_const: 1.0
|
||||
gae_lambda: 0.95
|
||||
batch_size: ${eval:'round(${train.n_steps} * ${env.n_envs} / 2)'}
|
||||
update_epochs: 10
|
||||
vf_coef: 0.5
|
||||
target_kl: 1
|
||||
|
||||
model:
|
||||
_target_: model.rl.gmm_ppo.PPO_GMM
|
||||
clip_ploss_coef: 0.1
|
||||
network_path: ${base_policy_path}
|
||||
actor:
|
||||
_target_: model.common.mlp_gmm.GMM_MLP
|
||||
mlp_dims: [256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: False
|
||||
fixed_std: 0.1
|
||||
learn_fixed_std: False
|
||||
num_modes: ${num_modes}
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
critic:
|
||||
_target_: model.common.critic.CriticObs
|
||||
mlp_dims: [256, 256, 256]
|
||||
residual_style: True
|
||||
obs_dim: ${obs_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,119 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.finetune.train_ppo_diffusion_agent.TrainPPODiffusionAgent
|
||||
|
||||
name: ${env_name}_m2_ft_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}_tdf${ft_denoising_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
base_policy_path: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/m2/avoid_d57_r12_pre_diffusion_mlp_ta4_td20/2024-07-07_13-12-09/checkpoint/state_15000.pt
|
||||
normalization_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m2/normalization.npz
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env_name: avoiding-m5
|
||||
mode: d57_r12 # M2, desired modes 5 and 7, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
denoising_steps: 20
|
||||
ft_denoising_steps: 10
|
||||
cond_steps: 1
|
||||
horizon_steps: 4
|
||||
act_steps: 4
|
||||
|
||||
env:
|
||||
n_envs: 50
|
||||
name: ${env_name}
|
||||
max_episode_steps: 100
|
||||
reset_at_iteration: True
|
||||
save_video: False
|
||||
best_reward_threshold_for_success: 2
|
||||
save_full_observations: True
|
||||
wrappers:
|
||||
d3il_lowdim:
|
||||
normalization_path: ${normalization_path}
|
||||
multi_step:
|
||||
n_obs_steps: ${cond_steps}
|
||||
n_action_steps: ${act_steps}
|
||||
max_episode_steps: ${env.max_episode_steps}
|
||||
pass_full_observations: ${env.save_full_observations}
|
||||
reset_within_step: False
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env_name}-m2-finetune
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_train_itr: 51
|
||||
n_critic_warmup_itr: 1
|
||||
n_steps: ${eval:'round(${env.max_episode_steps} / ${act_steps})'}
|
||||
gamma: 0.99
|
||||
actor_lr: 1e-5
|
||||
actor_weight_decay: 0
|
||||
actor_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-5
|
||||
critic_lr: 1e-3
|
||||
critic_weight_decay: 0
|
||||
critic_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-3
|
||||
save_model_freq: 100
|
||||
val_freq: 100 # no eval, always train mode
|
||||
force_train: True
|
||||
render:
|
||||
freq: 1
|
||||
num: 10
|
||||
plotter:
|
||||
_target_: env.plot_traj.TrajPlotter
|
||||
env_type: avoid
|
||||
normalization_path: ${normalization_path}
|
||||
# PPO specific
|
||||
reward_scale_running: True
|
||||
reward_scale_const: 1.0
|
||||
gae_lambda: 0.95
|
||||
batch_size: ${eval:'round(${train.n_steps} * ${env.n_envs} / 2)'}
|
||||
update_epochs: 10
|
||||
vf_coef: 0.5
|
||||
target_kl: 1
|
||||
|
||||
model:
|
||||
_target_: model.diffusion.diffusion_ppo.PPODiffusion
|
||||
# HP to tune
|
||||
gamma_denoising: 0.95
|
||||
clip_ploss_coef: 0.1
|
||||
clip_ploss_coef_base: 0.1
|
||||
clip_ploss_coef_rate: 1
|
||||
randn_clip_value: 3
|
||||
min_sampling_denoising_std: 0.1
|
||||
min_logprob_denoising_std: 0.1
|
||||
#
|
||||
network_path: ${base_policy_path}
|
||||
actor:
|
||||
_target_: model.diffusion.mlp_diffusion.DiffusionMLP
|
||||
time_dim: 16
|
||||
mlp_dims: [512, 512, 512]
|
||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
critic:
|
||||
_target_: model.common.critic.CriticObs
|
||||
obs_dim: ${obs_dim}
|
||||
mlp_dims: [256, 256, 256]
|
||||
activation_type: Mish
|
||||
residual_style: True
|
||||
ft_denoising_steps: ${ft_denoising_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
obs_dim: ${obs_dim}
|
||||
action_dim: ${action_dim}
|
||||
cond_steps: ${cond_steps}
|
||||
denoising_steps: ${denoising_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,105 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.finetune.train_ppo_gaussian_agent.TrainPPOGaussianAgent
|
||||
|
||||
name: ${env_name}_m2_gaussian_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
base_policy_path: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/m2/avoid_d57_r12_pre_gaussian_mlp_ta4/2024-07-07_02-15-50/checkpoint/state_10000.pt
|
||||
normalization_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m2/normalization.npz
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env_name: avoiding-m5
|
||||
mode: d57_r12 # M2, desired modes 5 and 7, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
cond_steps: 1
|
||||
horizon_steps: 4
|
||||
act_steps: 4
|
||||
|
||||
env:
|
||||
n_envs: 50
|
||||
name: ${env_name}
|
||||
max_episode_steps: 100
|
||||
reset_at_iteration: True
|
||||
save_video: False
|
||||
best_reward_threshold_for_success: 2
|
||||
save_full_observations: True
|
||||
wrappers:
|
||||
d3il_lowdim:
|
||||
normalization_path: ${normalization_path}
|
||||
multi_step:
|
||||
n_obs_steps: ${cond_steps}
|
||||
n_action_steps: ${act_steps}
|
||||
max_episode_steps: ${env.max_episode_steps}
|
||||
pass_full_observations: ${env.save_full_observations}
|
||||
reset_within_step: False
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env_name}-m2-finetune
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_train_itr: 51
|
||||
n_critic_warmup_itr: 1
|
||||
n_steps: ${eval:'round(${env.max_episode_steps} / ${act_steps})'}
|
||||
gamma: 0.99
|
||||
actor_lr: 1e-5
|
||||
actor_weight_decay: 0
|
||||
actor_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-5
|
||||
critic_lr: 1e-3
|
||||
critic_weight_decay: 0
|
||||
critic_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-3
|
||||
save_model_freq: 100
|
||||
val_freq: 100 # no eval, always train mode
|
||||
force_train: True
|
||||
render:
|
||||
freq: 1
|
||||
num: 10
|
||||
plotter:
|
||||
_target_: env.plot_traj.TrajPlotter
|
||||
env_type: avoid
|
||||
normalization_path: ${normalization_path}
|
||||
# PPO specific
|
||||
reward_scale_running: True
|
||||
reward_scale_const: 1.0
|
||||
gae_lambda: 0.95
|
||||
batch_size: ${eval:'round(${train.n_steps} * ${env.n_envs} / 2)'}
|
||||
update_epochs: 10
|
||||
vf_coef: 0.5
|
||||
target_kl: 1
|
||||
|
||||
model:
|
||||
_target_: model.rl.gaussian_ppo.PPO_Gaussian
|
||||
clip_ploss_coef: 0.1
|
||||
randn_clip_value: 3
|
||||
network_path: ${base_policy_path}
|
||||
actor:
|
||||
_target_: model.common.mlp_gaussian.Gaussian_MLP
|
||||
mlp_dims: [256, 256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
fixed_std: 0.1
|
||||
learn_fixed_std: False
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
critic:
|
||||
_target_: model.common.critic.CriticObs
|
||||
mlp_dims: [256, 256, 256]
|
||||
residual_style: True
|
||||
obs_dim: ${obs_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,106 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.finetune.train_ppo_gaussian_agent.TrainPPOGaussianAgent
|
||||
|
||||
name: ${env_name}_m2_gmm_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
base_policy_path: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/m2/avoid_d57_r12_pre_gmm_mlp_ta4/2024-07-10_15-44-32/checkpoint/state_10000.pt
|
||||
normalization_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m2/normalization.npz
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env_name: avoiding-m5
|
||||
mode: d57_r12 # M2, desired modes 5 and 7, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
cond_steps: 1
|
||||
horizon_steps: 4
|
||||
act_steps: 4
|
||||
num_modes: 5
|
||||
|
||||
env:
|
||||
n_envs: 50
|
||||
name: ${env_name}
|
||||
max_episode_steps: 100
|
||||
reset_at_iteration: True
|
||||
save_video: False
|
||||
best_reward_threshold_for_success: 2
|
||||
save_full_observations: True
|
||||
wrappers:
|
||||
d3il_lowdim:
|
||||
normalization_path: ${normalization_path}
|
||||
multi_step:
|
||||
n_obs_steps: ${cond_steps}
|
||||
n_action_steps: ${act_steps}
|
||||
max_episode_steps: ${env.max_episode_steps}
|
||||
pass_full_observations: ${env.save_full_observations}
|
||||
reset_within_step: False
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env_name}-m2-finetune
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_train_itr: 51
|
||||
n_critic_warmup_itr: 1
|
||||
n_steps: ${eval:'round(${env.max_episode_steps} / ${act_steps})'}
|
||||
gamma: 0.99
|
||||
actor_lr: 1e-5
|
||||
actor_weight_decay: 0
|
||||
actor_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-5
|
||||
critic_lr: 1e-3
|
||||
critic_weight_decay: 0
|
||||
critic_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-3
|
||||
save_model_freq: 100
|
||||
val_freq: 100 # no eval, always train mode
|
||||
force_train: True
|
||||
render:
|
||||
freq: 1
|
||||
num: 10
|
||||
plotter:
|
||||
_target_: env.plot_traj.TrajPlotter
|
||||
env_type: avoid
|
||||
normalization_path: ${normalization_path}
|
||||
# PPO specific
|
||||
reward_scale_running: True
|
||||
reward_scale_const: 1.0
|
||||
gae_lambda: 0.95
|
||||
batch_size: ${eval:'round(${train.n_steps} * ${env.n_envs} / 2)'}
|
||||
update_epochs: 10
|
||||
vf_coef: 0.5
|
||||
target_kl: 1
|
||||
|
||||
model:
|
||||
_target_: model.rl.gmm_ppo.PPO_GMM
|
||||
clip_ploss_coef: 0.1
|
||||
network_path: ${base_policy_path}
|
||||
actor:
|
||||
_target_: model.common.mlp_gmm.GMM_MLP
|
||||
mlp_dims: [256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: False
|
||||
fixed_std: 0.1
|
||||
learn_fixed_std: False
|
||||
num_modes: ${num_modes}
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
critic:
|
||||
_target_: model.common.critic.CriticObs
|
||||
mlp_dims: [256, 256, 256]
|
||||
residual_style: True
|
||||
obs_dim: ${obs_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,119 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.finetune.train_ppo_diffusion_agent.TrainPPODiffusionAgent
|
||||
|
||||
name: ${env_name}_m3_ft_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}_tdf${ft_denoising_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
base_policy_path: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/m3/avoid_d58_r12_pre_diffusion_mlp_ta4_td20/2024-07-07_13-54-54/checkpoint/state_15000.pt
|
||||
normalization_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m3/normalization.npz
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env_name: avoiding-m5
|
||||
mode: d58_r12 # M3, desired modes 5 and 8, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
denoising_steps: 20
|
||||
ft_denoising_steps: 10
|
||||
cond_steps: 1
|
||||
horizon_steps: 4
|
||||
act_steps: 4
|
||||
|
||||
env:
|
||||
n_envs: 50
|
||||
name: ${env_name}
|
||||
max_episode_steps: 100
|
||||
reset_at_iteration: True
|
||||
save_video: False
|
||||
best_reward_threshold_for_success: 2
|
||||
save_full_observations: True
|
||||
wrappers:
|
||||
d3il_lowdim:
|
||||
normalization_path: ${normalization_path}
|
||||
multi_step:
|
||||
n_obs_steps: ${cond_steps}
|
||||
n_action_steps: ${act_steps}
|
||||
max_episode_steps: ${env.max_episode_steps}
|
||||
pass_full_observations: ${env.save_full_observations}
|
||||
reset_within_step: False
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env_name}-m3-finetune
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_train_itr: 51
|
||||
n_critic_warmup_itr: 1
|
||||
n_steps: ${eval:'round(${env.max_episode_steps} / ${act_steps})'}
|
||||
gamma: 0.99
|
||||
actor_lr: 1e-5
|
||||
actor_weight_decay: 0
|
||||
actor_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-5
|
||||
critic_lr: 1e-3
|
||||
critic_weight_decay: 0
|
||||
critic_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-3
|
||||
save_model_freq: 100
|
||||
val_freq: 100 # no eval, always train mode
|
||||
force_train: True
|
||||
render:
|
||||
freq: 1
|
||||
num: 10
|
||||
plotter:
|
||||
_target_: env.plot_traj.TrajPlotter
|
||||
env_type: avoid
|
||||
normalization_path: ${normalization_path}
|
||||
# PPO specific
|
||||
reward_scale_running: True
|
||||
reward_scale_const: 1.0
|
||||
gae_lambda: 0.95
|
||||
batch_size: ${eval:'round(${train.n_steps} * ${env.n_envs} / 2)'}
|
||||
update_epochs: 10
|
||||
vf_coef: 0.5
|
||||
target_kl: 1
|
||||
|
||||
model:
|
||||
_target_: model.diffusion.diffusion_ppo.PPODiffusion
|
||||
# HP to tune
|
||||
gamma_denoising: 0.95
|
||||
clip_ploss_coef: 0.1
|
||||
clip_ploss_coef_base: 0.1
|
||||
clip_ploss_coef_rate: 1
|
||||
randn_clip_value: 3
|
||||
min_sampling_denoising_std: 0.1
|
||||
min_logprob_denoising_std: 0.1
|
||||
#
|
||||
network_path: ${base_policy_path}
|
||||
actor:
|
||||
_target_: model.diffusion.mlp_diffusion.DiffusionMLP
|
||||
time_dim: 16
|
||||
mlp_dims: [512, 512, 512]
|
||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
critic:
|
||||
_target_: model.common.critic.CriticObs
|
||||
obs_dim: ${obs_dim}
|
||||
mlp_dims: [256, 256, 256]
|
||||
activation_type: Mish
|
||||
residual_style: True
|
||||
ft_denoising_steps: ${ft_denoising_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
obs_dim: ${obs_dim}
|
||||
action_dim: ${action_dim}
|
||||
cond_steps: ${cond_steps}
|
||||
denoising_steps: ${denoising_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,105 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.finetune.train_ppo_gaussian_agent.TrainPPOGaussianAgent
|
||||
|
||||
name: ${env_name}_m3_gaussian_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
base_policy_path: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/m3/avoid_d58_r12_pre_gaussian_mlp_ta4/2024-07-07_13-11-49/checkpoint/state_10000.pt
|
||||
normalization_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m3/normalization.npz
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env_name: avoiding-m5
|
||||
mode: d58_r12 # M3, desired modes 5 and 8, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
cond_steps: 1
|
||||
horizon_steps: 4
|
||||
act_steps: 4
|
||||
|
||||
env:
|
||||
n_envs: 50
|
||||
name: ${env_name}
|
||||
max_episode_steps: 100
|
||||
reset_at_iteration: True
|
||||
save_video: False
|
||||
best_reward_threshold_for_success: 2
|
||||
save_full_observations: True
|
||||
wrappers:
|
||||
d3il_lowdim:
|
||||
normalization_path: ${normalization_path}
|
||||
multi_step:
|
||||
n_obs_steps: ${cond_steps}
|
||||
n_action_steps: ${act_steps}
|
||||
max_episode_steps: ${env.max_episode_steps}
|
||||
pass_full_observations: ${env.save_full_observations}
|
||||
reset_within_step: False
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env_name}-m3-finetune
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_train_itr: 51
|
||||
n_critic_warmup_itr: 1
|
||||
n_steps: ${eval:'round(${env.max_episode_steps} / ${act_steps})'}
|
||||
gamma: 0.99
|
||||
actor_lr: 1e-5
|
||||
actor_weight_decay: 0
|
||||
actor_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-5
|
||||
critic_lr: 1e-3
|
||||
critic_weight_decay: 0
|
||||
critic_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-3
|
||||
save_model_freq: 100
|
||||
val_freq: 100 # no eval, always train mode
|
||||
force_train: True
|
||||
render:
|
||||
freq: 1
|
||||
num: 10
|
||||
plotter:
|
||||
_target_: env.plot_traj.TrajPlotter
|
||||
env_type: avoid
|
||||
normalization_path: ${normalization_path}
|
||||
# PPO specific
|
||||
reward_scale_running: True
|
||||
reward_scale_const: 1.0
|
||||
gae_lambda: 0.95
|
||||
batch_size: ${eval:'round(${train.n_steps} * ${env.n_envs} / 2)'}
|
||||
update_epochs: 10
|
||||
vf_coef: 0.5
|
||||
target_kl: 1
|
||||
|
||||
model:
|
||||
_target_: model.rl.gaussian_ppo.PPO_Gaussian
|
||||
clip_ploss_coef: 0.1
|
||||
randn_clip_value: 3
|
||||
network_path: ${base_policy_path}
|
||||
actor:
|
||||
_target_: model.common.mlp_gaussian.Gaussian_MLP
|
||||
mlp_dims: [256, 256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
fixed_std: 0.1
|
||||
learn_fixed_std: False
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
critic:
|
||||
_target_: model.common.critic.CriticObs
|
||||
mlp_dims: [256, 256, 256]
|
||||
residual_style: True
|
||||
obs_dim: ${obs_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,106 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.finetune.train_ppo_gaussian_agent.TrainPPOGaussianAgent
|
||||
|
||||
name: ${env_name}_m3_gmm_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
base_policy_path: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/m3/avoid_d58_r12_pre_gmm_mlp_ta4/2024-07-10_17-01-50/checkpoint/state_10000.pt
|
||||
normalization_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m3/normalization.npz
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env_name: avoiding-m5
|
||||
mode: d58_r12 # M3, desired modes 5 and 8, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
cond_steps: 1
|
||||
horizon_steps: 4
|
||||
act_steps: 4
|
||||
num_modes: 5
|
||||
|
||||
env:
|
||||
n_envs: 50
|
||||
name: ${env_name}
|
||||
max_episode_steps: 100
|
||||
reset_at_iteration: True
|
||||
save_video: False
|
||||
best_reward_threshold_for_success: 2
|
||||
save_full_observations: True
|
||||
wrappers:
|
||||
d3il_lowdim:
|
||||
normalization_path: ${normalization_path}
|
||||
multi_step:
|
||||
n_obs_steps: ${cond_steps}
|
||||
n_action_steps: ${act_steps}
|
||||
max_episode_steps: ${env.max_episode_steps}
|
||||
pass_full_observations: ${env.save_full_observations}
|
||||
reset_within_step: False
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env_name}-m3-finetune
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_train_itr: 51
|
||||
n_critic_warmup_itr: 1
|
||||
n_steps: ${eval:'round(${env.max_episode_steps} / ${act_steps})'}
|
||||
gamma: 0.99
|
||||
actor_lr: 1e-5
|
||||
actor_weight_decay: 0
|
||||
actor_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-5
|
||||
critic_lr: 1e-3
|
||||
critic_weight_decay: 0
|
||||
critic_lr_scheduler:
|
||||
first_cycle_steps: 100
|
||||
warmup_steps: 10
|
||||
min_lr: 1e-3
|
||||
save_model_freq: 100
|
||||
val_freq: 100 # no eval, always train mode
|
||||
force_train: True
|
||||
render:
|
||||
freq: 1
|
||||
num: 10
|
||||
plotter:
|
||||
_target_: env.plot_traj.TrajPlotter
|
||||
env_type: avoid
|
||||
normalization_path: ${normalization_path}
|
||||
# PPO specific
|
||||
reward_scale_running: True
|
||||
reward_scale_const: 1.0
|
||||
gae_lambda: 0.95
|
||||
batch_size: ${eval:'round(${train.n_steps} * ${env.n_envs} / 2)'}
|
||||
update_epochs: 10
|
||||
vf_coef: 0.5
|
||||
target_kl: 1
|
||||
|
||||
model:
|
||||
_target_: model.rl.gmm_ppo.PPO_GMM
|
||||
clip_ploss_coef: 0.1
|
||||
network_path: ${base_policy_path}
|
||||
actor:
|
||||
_target_: model.common.mlp_gmm.GMM_MLP
|
||||
mlp_dims: [256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: False
|
||||
fixed_std: 0.1
|
||||
learn_fixed_std: False
|
||||
num_modes: ${num_modes}
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
critic:
|
||||
_target_: model.common.critic.CriticObs
|
||||
mlp_dims: [256, 256, 256]
|
||||
residual_style: True
|
||||
obs_dim: ${obs_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,70 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_diffusion_agent.TrainDiffusionAgent
|
||||
|
||||
name: avoid_m1_pre_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m1/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d56_r12 # M1, desired modes 5 and 6, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
denoising_steps: 20
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 15000
|
||||
batch_size: 16
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 15000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.diffusion.diffusion.DiffusionModel
|
||||
predict_epsilon: True
|
||||
denoised_clip_value: 1.0
|
||||
network:
|
||||
_target_: model.diffusion.mlp_diffusion.DiffusionMLP
|
||||
time_dim: 16
|
||||
mlp_dims: [512, 512, 512]
|
||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
obs_dim: ${obs_dim}
|
||||
action_dim: ${action_dim}
|
||||
transition_dim: ${transition_dim}
|
||||
denoising_steps: ${denoising_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,62 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: avoid_m1_pre_gaussian_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m1/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d56_r12 # M1, desired modes 5 and 6, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 10000
|
||||
batch_size: 16
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gaussian.GaussianModel
|
||||
network:
|
||||
_target_: model.common.mlp_gaussian.Gaussian_MLP
|
||||
mlp_dims: [256, 256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
fixed_std: 0.1
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,64 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: avoid_m1_pre_gmm_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m1/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d56_r12 # M1, desired modes 5 and 6, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
num_modes: 5
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 10000
|
||||
batch_size: 32
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gmm.GMMModel
|
||||
network:
|
||||
_target_: model.common.mlp_gmm.GMM_MLP
|
||||
mlp_dims: [256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: False
|
||||
fixed_std: 0.1
|
||||
num_modes: ${num_modes}
|
||||
cond_dim: ${obs_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,70 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_diffusion_agent.TrainDiffusionAgent
|
||||
|
||||
name: avoid_m2_pre_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m2/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d57_r12 # M2, desired modes 5 and 7, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
denoising_steps: 20
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 15000
|
||||
batch_size: 16
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 15000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.diffusion.diffusion.DiffusionModel
|
||||
predict_epsilon: True
|
||||
denoised_clip_value: 1.0
|
||||
network:
|
||||
_target_: model.diffusion.mlp_diffusion.DiffusionMLP
|
||||
time_dim: 16
|
||||
mlp_dims: [512, 512, 512]
|
||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
obs_dim: ${obs_dim}
|
||||
action_dim: ${action_dim}
|
||||
transition_dim: ${transition_dim}
|
||||
denoising_steps: ${denoising_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,62 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: avoid_m2_pre_gaussian_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m2/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d57_r12 # M2, desired modes 5 and 7, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 10000
|
||||
batch_size: 16
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gaussian.GaussianModel
|
||||
network:
|
||||
_target_: model.common.mlp_gaussian.Gaussian_MLP
|
||||
mlp_dims: [256, 256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
fixed_std: 0.1
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,64 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: avoid_m2_pre_gmm_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m2/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d57_r12 # M2, desired modes 5 and 7, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
num_modes: 5
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 10000
|
||||
batch_size: 32
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gmm.GMMModel
|
||||
network:
|
||||
_target_: model.common.mlp_gmm.GMM_MLP
|
||||
mlp_dims: [256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: False
|
||||
fixed_std: 0.1
|
||||
num_modes: ${num_modes}
|
||||
cond_dim: ${obs_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,70 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_diffusion_agent.TrainDiffusionAgent
|
||||
|
||||
name: avoid_m3_pre_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m3/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d58_r12 # M3, desired modes 5 and 8, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
denoising_steps: 20
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 15000
|
||||
batch_size: 16
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 15000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.diffusion.diffusion.DiffusionModel
|
||||
predict_epsilon: True
|
||||
denoised_clip_value: 1.0
|
||||
network:
|
||||
_target_: model.diffusion.mlp_diffusion.DiffusionMLP
|
||||
time_dim: 16
|
||||
mlp_dims: [512, 512, 512]
|
||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
obs_dim: ${obs_dim}
|
||||
action_dim: ${action_dim}
|
||||
transition_dim: ${transition_dim}
|
||||
denoising_steps: ${denoising_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,62 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: avoid_m3_pre_gaussian_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m3/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d58_r12 # M3, desired modes 5 and 8, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 10000
|
||||
batch_size: 16
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gaussian.GaussianModel
|
||||
network:
|
||||
_target_: model.common.mlp_gaussian.Gaussian_MLP
|
||||
mlp_dims: [256, 256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
fixed_std: 0.1
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,64 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: avoid_m3_pre_gmm_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m3/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d58_r12 # M3, desired modes 5 and 8, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
num_modes: 5
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 10000
|
||||
batch_size: 32
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gmm.GMMModel
|
||||
network:
|
||||
_target_: model.common.mlp_gmm.GMM_MLP
|
||||
mlp_dims: [256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: False
|
||||
fixed_std: 0.1
|
||||
num_modes: ${num_modes}
|
||||
cond_dim: ${obs_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
Reference in New Issue
Block a user