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Datasets:
conglu
/
vd4rl

ArXiv:
Tags:
Reinforcement Learning
Offline Reinforcement Learning
Reinforcement Learning from Pixels
DreamerV2
DrQ+BC
License:
Dataset card Files Community
vd4rl
File size: 4,630 Bytes 
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defaults:

  # Train Script
  logdir: /dev/null
  seed: 0
  task: dmc_walker_walk
  envs: 1
  envs_parallel: none
  render_size: [64, 64]
  dmc_camera: -1
  atari_grayscale: True
  time_limit: 0
  action_repeat: 1
  # steps: 1e7
  steps: 2e5
  log_every: 1e4
  eval_every: 1e5
  eval_eps: 1
  prefill: 10000
  pretrain: 1
  train_every: 5
  train_steps: 1
  expl_until: 0
  replay: {capacity: 2e6, ongoing: False, minlen: 50, maxlen: 50, prioritize_ends: True}
  dataset: {batch: 16, length: 50}
  log_keys_video: ['image']
  log_keys_sum: '^$'
  log_keys_mean: '^$'
  log_keys_max: '^$'
  precision: 16
  jit: True

  offline_dir: [none]
  offline_model_train_steps: 25001
  offline_model_loaddir: none
  offline_lmbd: 5.0
  offline_penalty_type: none
  offline_model_save_every: 5000
  offline_split_val: False
  offline_tune_lmbd: False
  offline_lmbd_cons: 1.5
  offline_model_dataset: {batch: 64, length: 50}
  offline_train_dataset: {batch: 64, length: 50}

  # Agent
  clip_rewards: tanh
  expl_behavior: greedy
  expl_noise: 0.0
  eval_noise: 0.0
  eval_state_mean: False

  # World Model
  grad_heads: [decoder, reward, discount]
  pred_discount: True
  rssm: {ensemble: 7, hidden: 1024, deter: 1024, stoch: 32, discrete: 32, act: elu, norm: none, std_act: sigmoid2, min_std: 0.1}
  encoder: {mlp_keys: '.*', cnn_keys: '.*', act: elu, norm: none, cnn_depth: 48, cnn_kernels: [4, 4, 4, 4], mlp_layers: [400, 400, 400, 400]}
  decoder: {mlp_keys: '.*', cnn_keys: '.*', act: elu, norm: none, cnn_depth: 48, cnn_kernels: [5, 5, 6, 6], mlp_layers: [400, 400, 400, 400]}
  reward_head: {layers: 4, units: 400, act: elu, norm: none, dist: mse}
  discount_head: {layers: 4, units: 400, act: elu, norm: none, dist: binary}
  loss_scales: {kl: 1.0, reward: 1.0, discount: 1.0, proprio: 1.0}
  kl: {free: 0.0, forward: False, balance: 0.8, free_avg: True}
  model_opt: {opt: adam, lr: 1e-4, eps: 1e-5, clip: 100, wd: 1e-6}

  # Actor Critic
  actor: {layers: 4, units: 400, act: elu, norm: none, dist: auto, min_std: 0.1}
  critic: {layers: 4, units: 400, act: elu, norm: none, dist: mse}
  actor_opt: {opt: adam, lr: 8e-5, eps: 1e-5, clip: 100, wd: 1e-6}
  critic_opt: {opt: adam, lr: 2e-4, eps: 1e-5, clip: 100, wd: 1e-6}
  discount: 0.99
  discount_lambda: 0.95
  imag_horizon: 5
  actor_grad: auto
  actor_grad_mix: 0.1
  actor_ent: 2e-3
  slow_target: True
  slow_target_update: 100
  slow_target_fraction: 1
  slow_baseline: True
  reward_norm: {momentum: 1.0, scale: 1.0, eps: 1e-8}

  # Exploration
  expl_intr_scale: 1.0
  expl_extr_scale: 0.0
  expl_opt: {opt: adam, lr: 3e-4, eps: 1e-5, clip: 100, wd: 1e-6}
  expl_head: {layers: 4, units: 400, act: elu, norm: none, dist: mse}
  expl_reward_norm: {momentum: 1.0, scale: 1.0, eps: 1e-8}
  disag_target: stoch
  disag_log: False
  disag_models: 10
  disag_offset: 1
  disag_action_cond: True
  expl_model_loss: kl

atari:

  task: atari_pong
  encoder: {mlp_keys: '$^', cnn_keys: 'image'}
  decoder: {mlp_keys: '$^', cnn_keys: 'image'}
  time_limit: 27000
  action_repeat: 4
  steps: 5e7
  eval_every: 2.5e5
  log_every: 1e4
  prefill: 50000
  train_every: 16
  clip_rewards: tanh
  rssm: {hidden: 600, deter: 600}
  model_opt.lr: 2e-4
  actor_opt.lr: 4e-5
  critic_opt.lr: 1e-4
  actor_ent: 1e-3
  discount: 0.999
  loss_scales.kl: 0.1
  loss_scales.discount: 5.0

crafter:

  task: crafter_reward
  encoder: {mlp_keys: '$^', cnn_keys: 'image'}
  decoder: {mlp_keys: '$^', cnn_keys: 'image'}
  log_keys_max: '^log_achievement_.*'
  log_keys_sum: '^log_reward$'
  discount: 0.999
  .*\.norm: layer

dmc_vision:

  task: dmc_walker_walk
  encoder: {mlp_keys: '$^', cnn_keys: 'image'}
  decoder: {mlp_keys: '$^', cnn_keys: 'image'}
  action_repeat: 2
  eval_every: 1e4
  prefill: 1000
  pretrain: 100
  clip_rewards: identity
  pred_discount: False
  replay.prioritize_ends: False
  grad_heads: [decoder, reward]
  rssm: {hidden: 200, deter: 200}
  model_opt.lr: 3e-4
  actor_opt.lr: 8e-5
  critic_opt.lr: 8e-5
  actor_ent: 1e-4
  kl.free: 1.0

dmc_proprio:

  task: dmc_walker_walk
  encoder: {mlp_keys: '.*', cnn_keys: '$^'}
  decoder: {mlp_keys: '.*', cnn_keys: '$^'}
  action_repeat: 2
  eval_every: 1e4
  prefill: 1000
  pretrain: 100
  clip_rewards: identity
  pred_discount: False
  replay.prioritize_ends: False
  grad_heads: [decoder, reward]
  rssm: {hidden: 200, deter: 200}
  model_opt.lr: 3e-4
  actor_opt.lr: 8e-5
  critic_opt.lr: 8e-5
  actor_ent: 1e-4
  kl.free: 1.0

debug:

  jit: False
  time_limit: 100
  eval_every: 300
  log_every: 300
  prefill: 100
  pretrain: 1
  train_steps: 1
  replay: {minlen: 10, maxlen: 30}
  dataset: {batch: 10, length: 10}