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configs.yaml
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configs.yaml
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defaults:
logdir: logs
id: default
traindir: ''
evaldir: ''
offline_traindir: ''
offline_evaldir: ''
offline_data_path: ''
save_eps: True
seed: 0
steps: 5e5
eval_every: 1e4
log_every: 1e4
reset_every: 0
gpu_growth: True
precision: 16
debug: False
expl_gifs: False
use_wandb: True
wandb_project: 'dreamer'
only_eval: False
only_eval_length: 500
n_eval_eps: 1
mode: 'train'
probe_features_type: 'post'
# Environment
task: 'dmc_walker_walk'
size: [64, 64]
envs: 1
action_repeat: 2
time_limit: 1000
grayscale: False
prefill: 2500
eval_noise: 0.0
clip_rewards: 'identity'
# Model
model_type: 'rssm'
dyn_cell: 'gru'
dyn_hidden: 200
dyn_deter: 200
dyn_stoch: 32
dyn_discrete: 0
dyn_input_layers: 1
dyn_output_layers: 1
dyn_rec_depth: 1
dyn_shared: False
dyn_mean_act: 'none'
dyn_std_act: 'sigmoid2'
dyn_min_std: 0.1
dyn_temp_post: True
dyn_kl_mask: False
dyn_gate_prior: 0.4
dyn_gate_free: 0.1
dyn_gate_scale: 0.0
grad_heads: ['image', 'reward']
units: 400
reward_layers: 2
discount_layers: 3
value_layers: 3
actor_layers: 4
act: 'elu'
cnn_depth: 32
encoder_kernels: [4, 4, 4, 4]
decoder_kernels: [5, 5, 6, 6]
decoder_thin: True
value_head: 'normal'
kl_scale: '1.0'
kl_balance: '0.8'
kl_free: '1.0'
kl_forward: False
kl_post: False
pred_discount: False
discount_scale: 1.0
reward_scale: 1.0
weight_decay: 0.0
# Training
batch_size: 50
batch_length: 50
train_every: 5
train_steps: 1
pretrain: 100
model_lr: 3e-4
value_lr: 8e-5
actor_lr: 8e-5
opt_eps: 1e-5
grad_clip: 100
value_grad_clip: 100
actor_grad_clip: 100
dataset_size: 0
oversample_ends: False
slow_value_target: True
slow_actor_target: True
slow_target_update: 100
slow_target_fraction: 1
opt: 'adam'
# Behavior.
discount: 0.99
discount_lambda: 0.95
imag_horizon: 15
imag_gradient: 'dynamics'
imag_gradient_mix: '0.1'
imag_sample: True
actor_dist: 'trunc_normal'
actor_entropy: '1e-4'
actor_state_entropy: 0.0
actor_init_std: 1.0
actor_min_std: 0.1
actor_disc: 5
actor_temp: 0.1
actor_outscale: 0.0
expl_amount: 0.0
eval_state_mean: False
collect_dyn_sample: True
behavior_stop_grad: True
value_decay: 0.0
future_entropy: False
# Exploration
expl_behavior: 'greedy'
expl_until: 0
expl_extr_scale: 0.0
expl_intr_scale: 1.0
disag_target: 'stoch'
disag_log: True
disag_models: 10
disag_offset: 1
disag_layers: 4
disag_units: 400
disag_action_cond: False
bringbackshapes:
steps: 1e6
eval_every: 5e4
log_every: 5e4
wandb_project: 'bringbackshapes'
n_eval_eps: 10
# Environment
task: 'bringbackshapes2D_sparse'
action_repeat: 4
time_limit: 3000
prefill: 10000
max_distractors: 2
max_objects: 5
variable_num_objects: False
variable_num_distractors: False
variable_goal_position: False
agent_view_size: 125
arena_scale: 1.0
# Model
dyn_cell: 'gru_layer_norm'
dyn_hidden: 1024
dyn_deter: 1024
dyn_stoch: 32
dyn_discrete: 32
grad_heads: ['image', 'reward', 'discount']
reward_layers: 4
discount_layers: 4
value_layers: 4
actor_layers: 4
cnn_depth: 48
kl_free: '0.0'
pred_discount: True
weight_decay: 1e-6
# Training
batch_size: 16
batch_length: 50
pretrain: 1
model_lr: 1e-4
value_lr: 2e-4
actor_lr: 8e-5
oversample_ends: True
# Behavior
actor_entropy: '2e-3'
bringbackshapes_gaussian:
dyn_discrete: 0
kl_free: 1.0
bringbackshapes_svsg:
dyn_cell: 'sgru'
dyn_stoch: 1024
dyn_discrete: 0
dyn_kl_mask: True
model_type: 'srssm'
kl_free: 1.0
bringbackshapes3D:
task: 'bringbackshapes3D_sparse'
dmc:
# Environment
task: 'dmc_walker_walk'
time_limit: 1000
action_repeat: 2
# Model
dyn_cell: 'gru_layer_norm'
cnn_depth: 32
dyn_deter: 200
dyn_stoch: 32
dyn_discrete: 0
reward_layers: 2
value_layers: 3
actor_layers: 4
reward_scale: 2
# Training
dataset_size: 0
pretrain: 100
prefill: 2500
# Behavior
actor_dist: 'trunc_normal'
actor_entropy: '1e-4'
imag_gradient_mix: 1.0 # Do we want to keep it 1.0?
# Training
model_lr: 3e-4
value_lr: 8e-5
actor_lr: 8e-5
debug:
debug: True
pretrain: 1
prefill: 1
train_steps: 1
batch_size: 10
batch_length: 20