dc8e0c9edc
* Sampling over both env and denoising steps in DPPO updates (#13) * sample one from each chain * full random sampling * Add Proficient Human (PH) Configs and Pipeline (#16) * fix missing cfg * add ph config * fix how terminated flags are added to buffer in ibrl * add ph config * offline calql for 1M gradient updates * bug fix: number of calql online gradient steps is the number of new transitions collected * add sample config for DPPO with ta=1 * Sampling over both env and denoising steps in DPPO updates (#13) * sample one from each chain * full random sampling * fix diffusion loss when predicting initial noise * fix dppo inds * fix typo * remove print statement --------- Co-authored-by: Justin M. Lidard <jlidard@neuronic.cs.princeton.edu> Co-authored-by: allenzren <allen.ren@princeton.edu> * update robomimic configs * better calql formulation * optimize calql and ibrl training * optimize data transfer in ppo agents * add kitchen configs * re-organize config folders, rerun calql and rlpd * add scratch gym locomotion configs * add kitchen installation dependencies * use truncated for termination in furniture env * update furniture and gym configs * update README and dependencies with kitchen * add url for new data and checkpoints * update demo RL configs * update batch sizes for furniture unet configs * raise error about dropout in residual mlp * fix observation bug in bc loss --------- Co-authored-by: Justin Lidard <60638575+jlidard@users.noreply.github.com> Co-authored-by: Justin M. Lidard <jlidard@neuronic.cs.princeton.edu>
122 lines
3.2 KiB
YAML
122 lines
3.2 KiB
YAML
defaults:
|
|
- _self_
|
|
hydra:
|
|
run:
|
|
dir: ${logdir}
|
|
_target_: agent.finetune.train_calql_agent.TrainCalQLAgent
|
|
|
|
name: ${env_name}_calql_mlp_ta${horizon_steps}
|
|
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
|
base_policy_path: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/can_calql_mlp_ta1/2024-10-25_22-30-16_42/checkpoint/state_999.pt
|
|
robomimic_env_cfg_path: cfg/robomimic/env_meta/${env_name}.json
|
|
normalization_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env_name}/normalization.npz
|
|
offline_dataset_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env_name}/train.npz
|
|
|
|
seed: 42
|
|
device: cuda:0
|
|
env_name: can
|
|
obs_dim: 23
|
|
action_dim: 7
|
|
cond_steps: 1
|
|
horizon_steps: 1
|
|
act_steps: 1
|
|
|
|
env:
|
|
n_envs: 1
|
|
name: ${env_name}
|
|
best_reward_threshold_for_success: 1
|
|
max_episode_steps: 300
|
|
reset_at_iteration: False
|
|
save_video: False
|
|
wrappers:
|
|
robomimic_lowdim:
|
|
normalization_path: ${normalization_path}
|
|
low_dim_keys: ['robot0_eef_pos',
|
|
'robot0_eef_quat',
|
|
'robot0_gripper_qpos',
|
|
'object'] # same order of preprocessed observations
|
|
multi_step:
|
|
n_obs_steps: ${cond_steps}
|
|
n_action_steps: ${act_steps}
|
|
max_episode_steps: ${env.max_episode_steps}
|
|
reset_within_step: True
|
|
|
|
wandb:
|
|
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
|
project: calql-${env_name}
|
|
run: ${now:%H-%M-%S}_${name}
|
|
|
|
train:
|
|
n_train_itr: 10000
|
|
n_steps: 1 # not used
|
|
n_episode_per_epoch: 1
|
|
gamma: 0.99
|
|
actor_lr: 1e-4
|
|
actor_weight_decay: 0
|
|
actor_lr_scheduler:
|
|
first_cycle_steps: 1000
|
|
warmup_steps: 10
|
|
min_lr: 1e-4
|
|
critic_lr: 3e-4
|
|
critic_weight_decay: 0
|
|
critic_lr_scheduler:
|
|
first_cycle_steps: 1000
|
|
warmup_steps: 10
|
|
min_lr: 3e-4
|
|
save_model_freq: 100
|
|
val_freq: 10
|
|
render:
|
|
freq: 1
|
|
num: 0
|
|
log_freq: 1
|
|
# CalQL specific
|
|
train_online: True
|
|
batch_size: 256
|
|
n_random_actions: 4
|
|
target_ema_rate: 0.005
|
|
scale_reward_factor: 1.0
|
|
num_update: 1000
|
|
buffer_size: 1000000
|
|
online_utd_ratio: 1
|
|
n_eval_episode: 40
|
|
n_explore_steps: 0
|
|
target_entropy: ${eval:'- ${action_dim} * ${act_steps}'}
|
|
init_temperature: 1
|
|
automatic_entropy_tuning: True
|
|
|
|
model:
|
|
_target_: model.rl.gaussian_calql.CalQL_Gaussian
|
|
randn_clip_value: 3
|
|
cql_min_q_weight: 5.0
|
|
tanh_output: True
|
|
network_path: ${base_policy_path}
|
|
actor:
|
|
_target_: model.common.mlp_gaussian.Gaussian_MLP
|
|
mlp_dims: [512, 512, 512]
|
|
activation_type: ReLU
|
|
tanh_output: False # squash after sampling instead
|
|
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
|
horizon_steps: ${horizon_steps}
|
|
action_dim: ${action_dim}
|
|
std_max: 7.3891
|
|
std_min: 0.0067
|
|
critic:
|
|
_target_: model.common.critic.CriticObsAct
|
|
mlp_dims: [256, 256, 256]
|
|
activation_type: ReLU
|
|
use_layernorm: True
|
|
double_q: True
|
|
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
|
action_dim: ${action_dim}
|
|
action_steps: ${act_steps}
|
|
horizon_steps: ${horizon_steps}
|
|
device: ${device}
|
|
|
|
offline_dataset:
|
|
_target_: agent.dataset.sequence.StitchedSequenceQLearningDataset
|
|
dataset_path: ${offline_dataset_path}
|
|
horizon_steps: ${horizon_steps}
|
|
cond_steps: ${cond_steps}
|
|
device: ${device}
|
|
discount_factor: ${train.gamma}
|
|
get_mc_return: True |