105 lines
2.8 KiB
YAML
105 lines
2.8 KiB
YAML
defaults:
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- _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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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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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}
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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:
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d3il_lowdim:
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normalization_path: ${normalization_path}
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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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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.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
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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} |