| action_repeat: 4 | |
| actor_critic_batch_size: 64 | |
| actor_critic_steps_per_epoch: 50 | |
| actor_hidden_size: 512 | |
| actor_layers: 1 | |
| actor_learning_rate: 0.0001 | |
| adam_beta1: 0.9 | |
| adam_beta2: 0.999 | |
| atari_100k: true | |
| autoencoder_batch_size: 64 | |
| burn_in_length: 20 | |
| checkpoint_interval: 50 | |
| collect_epsilon: 0.01 | |
| collect_temperature: 1.0 | |
| collection_epochs: 500 | |
| commitment_weight: 1.0 | |
| decoder_depth: 64 | |
| discount: 0.995 | |
| early_stopping: false | |
| encoder_channels: 64 | |
| encoder_layers: 4 | |
| encoder_residual_blocks: 2 | |
| entropy_coef: 0.001 | |
| env: ALE/Pong-v5 | |
| env_backend: gym | |
| env_steps_per_epoch: 200 | |
| eval_episodes: 100 | |
| eval_temperature: 0.5 | |
| frame_channels: 3 | |
| frame_height: 64 | |
| frame_width: 64 | |
| grad_clip_norm: 10.0 | |
| gradient_checkpointing: true | |
| imagination_horizon: 20 | |
| log_interval: 1000 | |
| max_env_steps: 100000 | |
| max_episode_steps: 27000 | |
| min_delta: 0.0001 | |
| model_learning_rate: 0.0001 | |
| patience: 10 | |
| perceptual_blocks: 5 | |
| perceptual_linear_weights: '' | |
| perceptual_weight: 1.0 | |
| quantizer: gradient | |
| reconstruction_weight: 1.0 | |
| repeat_action_probability: 0.0 | |
| reward_loss: cross_entropy | |
| reward_transform: sign | |
| start_actor_critic_after: 50 | |
| start_autoencoder_after: 5 | |
| start_transformer_after: 25 | |
| td_lambda: 0.95 | |
| token_embedding_dim: 512 | |
| tokens_per_frame: 16 | |
| total_epochs: 600 | |
| training_steps_per_epoch: 200 | |
| transformer_batch_size: 16 | |
| transformer_dropout: 0.1 | |
| transformer_embed_dim: 256 | |
| transformer_heads: 4 | |
| transformer_layers: 10 | |
| transformer_steps_per_epoch: 200 | |
| transformer_timesteps: 20 | |
| use_amp: true | |
| value_learning_rate: 0.0001 | |
| vocab_size: 512 | |
| weight_decay: 0.01 | |