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configs.yaml
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configs.yaml
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defaults:
seed: 0
method: name
task: dummy_disc
logdir: ~/d3_logdir
replay: uniform
replay_size: 1e6
replay_online: False
replay_priority_p: 0.1
replay_priority_thresh: 50
eval_dir: ''
filter: '.*'
jax:
platform: gpu
jit: True
precision: float16
prealloc: True
debug_nans: False
logical_cpus: 0
debug: False
policy_devices: [0]
train_devices: [0]
sync_every: 10
profiler: True
# transfer_guard: True # TODO: fix where it is violated.
transfer_guard: False
run:
script: train_eval
steps: 1e10
expl_until: 0
log_every: 900
save_every: 7200
eval_every: 2.5e4
eval_initial: True
eval_eps: 1
eval_samples: 1
train_ratio: 64.0
train_fill: 0
eval_fill: 0
log_zeros: True
log_keys_video: [image, fullsize_image]
log_keys_sum: '(reward)'
log_keys_mean: '(log_entropy)'
log_keys_eval_report: '(openl_)'
log_keys_max: '^$'
log_keys_last: '^$'
log_keys_trajectory: '^$'
log_keys_success: '^$'
log_video_streams: 1
log_video_timeout: 60
wandb: False
wandb_entity: entity1
wandb_project: project1
from_checkpoint: ''
actor_host: 'localhost'
actor_port: '5551'
actor_batch: 32
actor_threads: 1
env_replica: -1
ipv6: False
trace_malloc: False
is_train_behavior: True
rew_smoothing_mode: gaussian
rew_smoothing_amt: 0.0
envs: {amount: 4, parallel: process, length: 0, reset: True, restart: True, discretize: 0, checks: False}
wrapper: {length: 0, reset: True, discretize: 0, checks: False, resize: 0}
env:
atari: {size: [64, 64], repeat: 4, sticky: True, gray: True, actions: all, lives: unused, noops: 0, pooling: 2, aggregate: max, resize: pillow}
atari100k: {size: [64, 64], repeat: 4, sticky: False, gray: False, actions: needed, lives: unused, noops: 30, resize: pillow}
dmlab: {size: [64, 64], repeat: 4, episodic: True}
minecraft: {size: [64, 64], break_speed: 100.0, logs: False}
dmc: {size: [64, 64], repeat: 2, camera: -1}
loconav: {size: [64, 64], repeat: 2, camera: -1}
agx: {height: 64, width: 64, repeat: 4}
robodesk: {repeat: 8, length: 300, image_size: 64}
hand: {height: 64, width: 64}
# Agent
task_behavior: Greedy
expl_behavior: None
batch_size: 16
batch_length: 64
eval_batch_size: 16
eval_batch_length: 64
data_loaders: 8
# World Model
grad_heads: [decoder, reward, cont]
rssm_type: rssm
rssm: {impl: softmax, deter: 4096, units: 1024, stoch: 32, classes: 32, act: silu, norm: layer, unimix: 0.01, unroll: False, action_clip: 1.0, bottleneck: -1, winit: normal, fan: avg, maskgit: {embed: 256, layers: 4, heads: 4, ffwdim: 256, steps: 4}}
encoder: {mlp_keys: '.*', cnn_keys: '.*', act: silu, norm: layer, mlp_layers: 5, mlp_units: 1024, cnn: resnet, cnn_depth: 96, cnn_blocks: 0, resize: stride, winit: normal, fan: avg, symlog_inputs: True, minres: 4}
decoder: {mlp_keys: '.*', cnn_keys: '.*', act: silu, norm: layer, mlp_layers: 5, mlp_units: 1024, cnn: resnet, cnn_depth: 96, cnn_blocks: 0, image_dist: mse, vector_dist: symlog_mse, inputs: [deter, stoch], resize: stride, winit: normal, fan: avg, outscale: 1.0, minres: 4, cnn_sigmoid: False}
reward_head: {layers: 5, units: 1024, act: silu, norm: layer, dist: symlog_and_twohot, outscale: 0.0, outnorm: False, inputs: [deter, stoch], winit: normal, fan: avg, bins: 255}
cont_head: {layers: 5, units: 1024, act: silu, norm: layer, dist: binary, outscale: 1.0, outnorm: False, inputs: [deter, stoch], winit: normal, fan: avg}
loss_scales: {image: 1.0, vector: 1.0, reward: 1.0, cont: 1.0, dyn: 0.5, rep: 0.1, actor: 1.0, critic: 1.0, slowreg: 1.0}
rssm_loss: {free: 1.0}
model_opt: {opt: adam, lr: 1e-4, eps: 1e-8, clip: 1000.0, wd: 0.0, warmup: 0, lateclip: 0.0}
# Actor Critic
actor: {layers: 5, units: 1024, act: silu, norm: layer, minstd: 0.1, maxstd: 1.0, outscale: 1.0, outnorm: False, unimix: 0.01, inputs: [deter, stoch], winit: normal, fan: avg, symlog_inputs: False}
critic: {layers: 5, units: 1024, act: silu, norm: layer, dist: symlog_and_twohot, outscale: 0.0, outnorm: False, inputs: [deter, stoch], winit: normal, fan: avg, bins: 255, symlog_inputs: False}
actor_opt: {opt: adam, lr: 3e-5, eps: 1e-5, clip: 100.0, wd: 0.0, warmup: 0, lateclip: 0.0}
critic_opt: {opt: adam, lr: 3e-5, eps: 1e-5, clip: 100.0, wd: 0.0, warmup: 0, lateclip: 0.0}
actor_dist_disc: onehot
actor_dist_cont: normal
actor_grad_disc: reinforce
actor_grad_cont: backprop
critic_type: vfunction
imag_horizon: 15
imag_unroll: False
imag_cont: mode
horizon: 333
return_lambda: 0.95
critic_slowreg: logprob
slow_critic_update: 1
slow_critic_fraction: 0.02
slow_critic_target: False
retnorm: {impl: perc_ema, decay: 0.99, max: 1.0, perclo: 5.0, perchi: 95.0}
actent: 3e-4
# Exploration
expl_rewards: {extr: 1.0, disag: 0.1}
expl_opt: {opt: adam, lr: 1e-4, eps: 1e-5, clip: 100.0, wd: 0.0, warmup: 0}
disag_head: {layers: 5, units: 1024, act: silu, norm: layer, dist: mse, outscale: 1.0, inputs: [deter, stoch, action], winit: normal, fan: avg}
disag_target: [stoch]
disag_models: 8
# Director
director_jointly: True
train_skill_duration: 8
env_skill_duration: 8
goal_enc: {layers: 5, units: 1024, act: silu, norm: layer, dist: onehot, outscale: 1.0, inputs: [goal]}
goal_dec: {layers: 5, units: 1024, act: silu, norm: layer, dist: mse, outscale: 0.1, inputs: [skill]}
goal_opt: {opt: adam, lr: 1e-4, eps: 1e-6, clip: 100.0, wd: 1e-2, wd_pattern: 'kernel'}
goal_kl_scale: 1.0
goal_kl_free: 1.0
skill_shape: [8, 8]
manager_rews: {extr: 1.0, expl: 0.1, goal: 0.0}
manager_actent: 3e-4
worker_rews: {extr: 0.0, expl: 0.0, goal: 1.0}
worker_actent: 3e-4
worker_inputs: [deter, stoch, goal]
worker_goals: [manager]
worker_report_horizon: 64
# Director + Dreamer v3
advnorm: {impl: off, decay: 0.99, max: 1.0, perclo: 5.0, perchi: 95.0} # perc_ema used for Director + Dreamer v2
actent_norm: False
actent_norm_cfg: {impl: mult, scale: 3e-3, target: 0.5, min: 1e-5, max: 1e2, vel: 0.1}
# Dream High
imag_horizon_high: 8
wm_high: {layers: 5, units: 1024, act: silu, norm: layer, dist: symlog_mse, outscale: 0.1, inputs: [deter, action]}
wm_high_opt: {opt: adam, lr: 1e-4, eps: 1e-6, clip: 100.0, wd: 1e-2, wd_pattern: 'kernel'}
rew_high_head: {layers: 5, units: 1024, act: silu, norm: layer, dist: symlog_and_twohot, outscale: 0.0, outnorm: False, inputs: [deter], winit: normal, fan: avg, bins: 255}
rew_high_opt: {opt: adam, lr: 1e-4, eps: 1e-6, clip: 100.0, wd: 1e-2, wd_pattern: 'kernel'}
cont_high_head: {layers: 5, units: 1024, act: silu, norm: layer, dist: binary, outscale: 1.0, outnorm: False, inputs: [deter], winit: normal, fan: avg}
cont_high_opt: {opt: adam, lr: 1e-4, eps: 1e-6, clip: 100.0, wd: 1e-2, wd_pattern: 'kernel'}
manager_inputs: [deter]
minecraft:
task: minecraft_diamond
envs.amount: 16
run:
script: train_save
eval_fill: 1e5
train_ratio: 16
log_keys_max: '^log_inventory.*'
encoder: {mlp_keys: 'inventory|inventory_max|equipped|health|hunger|breath|reward', cnn_keys: 'image'}
decoder: {mlp_keys: 'inventory|inventory_max|equipped|health|hunger|breath', cnn_keys: 'image'}
dmlab:
task: dmlab_explore_goal_locations_small
envs.amount: 8
encoder: {mlp_keys: '$^', cnn_keys: 'image'}
decoder: {mlp_keys: '$^', cnn_keys: 'image'}
run.train_ratio: 64
atari:
task: atari_pong
envs.amount: 8
run:
steps: 5.5e7
eval_eps: 10
train_ratio: 64
encoder: {mlp_keys: '$^', cnn_keys: 'image'}
decoder: {mlp_keys: '$^', cnn_keys: 'image'}
atari100k:
task: atari_pong
envs: {amount: 1}
run:
script: train_eval
steps: 1.5e5
eval_every: 1e5
eval_initial: False
eval_eps: 100
train_ratio: 1024
jax.precision: float32
rssm.deter: 512
.*\.cnn_depth: 32
.*\.layers: 2
.*\.units$: 512
# actor_eval_sample: True
encoder: {mlp_keys: '$^', cnn_keys: 'image'}
decoder: {mlp_keys: '$^', cnn_keys: 'image'}
crafter:
task: crafter_reward
envs.amount: 1
run:
log_keys_max: '^log_achievement_.*'
log_keys_sum: '^log_reward$'
log_keys_trajectory: '^log_achievement'
log_keys_success: '^log_achievement'
run.train_ratio: 512
encoder: {mlp_keys: '$^', cnn_keys: 'image'}
decoder: {mlp_keys: '$^', cnn_keys: 'image'}
dmc_vision:
task: dmc_walker_walk
run.train_ratio: 512
rssm.deter: 512
.*\.cnn_depth: 32
.*\.layers: 2
.*\.units: 512
encoder: {mlp_keys: '$^', cnn_keys: 'image'}
decoder: {mlp_keys: '$^', cnn_keys: 'image'}
dmc_proprio:
task: dmc_walker_walk
run.train_ratio: 512
rssm.deter: 512
.*\.cnn_depth: 32
.*\.layers: 2
.*\.units: 512
encoder: {mlp_keys: '.*', cnn_keys: '$^'}
decoder: {mlp_keys: '.*', cnn_keys: '$^'}
bsuite:
task: bsuite_mnist/0
envs: {amount: 1, parallel: none}
run:
script: train
train_ratio: 1024 # 128 for cartpole
rssm.deter: 512
.*\.cnn_depth: 32
.*\.layers: 2
.*\.units: 512
pinpad:
task: pinpad_six
run.train_ratio: 32
rssm.deter: 512
.*\.cnn_depth: 32
.*\.layers: 2
.*\.units: 512
encoder: {mlp_keys: '$^', cnn_keys: 'image'}
decoder: {mlp_keys: '$^', cnn_keys: 'image'}
loconav:
task: loconav_ant_maze_s
env.loconav.repeat: 1 # repeat 1 used in Director
run:
train_ratio: 64
# train_ratio: 512
log_keys_max: '^log_.*'
rssm.deter: 1024
.*\.cnn_depth: 64
.*\.layers: 4
.*\.units: 512
# encoder: {mlp_keys: '.*', cnn_keys: 'image'}
# decoder: {mlp_keys: '.*', cnn_keys: 'image'}
encoder: {mlp_keys: '.*(joints|sensors|actuator|effectors|appendages|bodies|height|zaxis).*', cnn_keys: 'image'}
decoder: {mlp_keys: '.*(joints|sensors|actuator|effectors|appendages|bodies|height|zaxis).*', cnn_keys: 'image'}
kitchen:
task: kitchen_kitchen-mlsh-v0
run.train_ratio: 32
rssm.deter: 512
.*\.cnn_depth: 32
.*\.layers: 2
.*\.units: 512
encoder: {mlp_keys: '$^', cnn_keys: 'image'}
decoder: {mlp_keys: '$^', cnn_keys: 'image'}
agx:
task: agx_agx-pixel-rocks-v1
run:
log_keys_max: '^i_.*'
log_keys_last: '^i_.*'
train_ratio: 64
log_keys_video: [image1, image2, fullsize_image1, fullsize_image2]
rssm.deter: 2048
.*\.cnn_depth: 64
.*\.units: 768
.*\.layers: 4
encoder: {mlp_keys: 'ob', cnn_keys: '(image1|image2)'}
decoder: {mlp_keys: 'ob', cnn_keys: '(image1|image2)'}
replay_priority_thresh: 500
agx-singleview:
task: agx_agx-pixel-rocks-singleview-v3
run:
log_keys_max: '^i_.*'
log_keys_last: '^i_.*'
train_ratio: 64
log_keys_video: [image1]
rssm.deter: 512
.*\.cnn_depth: 32
.*\.layers: 2
.*\.units: 512
encoder: {mlp_keys: 'ob', cnn_keys: '(image1)'}
decoder: {mlp_keys: 'ob', cnn_keys: '(image1)'}
robodesk:
task: robodesk_yes
run:
steps: 3e6
log_keys_max: '^i_.*'
train_ratio: 64
rssm.deter: 2048
.*\.cnn_depth: 64
.*\.units: 768
.*\.layers: 4
encoder: {mlp_keys: '$^', cnn_keys: 'image'}
decoder: {mlp_keys: '$^', cnn_keys: 'image'}
replay_priority_thresh: 50
hand:
task: hand_RotateZ_dense
wrapper.length: 300
run:
steps: 20e6
log_keys_max: '^i_.*'
train_ratio: 64
rssm.deter: 2048
.*\.cnn_depth: 64
.*\.units: 768
.*\.layers: 4
encoder: {mlp_keys: '$^', cnn_keys: 'image'}
decoder: {mlp_keys: '$^', cnn_keys: 'image'}
replay_priority_thresh: 50
no_smoothing:
run:
rew_smoothing_mode: None
rew_smoothing_amt: 0
exp_smoothing:
run:
rew_smoothing_mode: exponential
rew_smoothing_amt: 0.33
gaussian_smoothing:
run:
rew_smoothing_mode: gaussian
rew_smoothing_amt: 3
uniform_smoothing:
run:
rew_smoothing_mode: uniform
rew_smoothing_amt: 9
uniform_before_smoothing:
run:
rew_smoothing_mode: uniform_before
rew_smoothing_amt: 5
uniform_after_smoothing:
run:
rew_smoothing_mode: uniform_after
rew_smoothing_amt: 5
supervised:
# eval_batch_size: 4
# eval_batch_length: 64
run:
script: train_supervised
is_train_behavior: False
log_every: 900
save_every: 3600
eval_every: 5e3
eval:
envs.amount: 1
wrapper.resize: 64
run:
script: eval_only
log_every: 10
steps: 10000
env:
atari.size: [64, 64]
atari100k.size: [64, 64]
dmlab.size: [64, 64]
minecraft.size: [64, 64]
dmc.size: [64, 64]
loconav.size: [64, 64]
agx:
height: 1024
width: 1024
robodesk.image_size: 480
hand:
height: 1024
width: 1024
morelogs:
run:
log_every: 10
save_every: 20
xxxxxsmallrew:
reward_head.layers: 1
reward_head.units: 16
xxxxsmallrew:
reward_head.layers: 1
reward_head.units: 32
xxxsmallrew:
reward_head.layers: 2
reward_head.units: 128
xxsmallrew:
reward_head.layers: 2
reward_head.units: 256
xsmallrew:
reward_head.layers: 2
reward_head.units: 512
smallrew:
reward_head.layers: 3
reward_head.units: 640
largerew:
reward_head.layers: 5
reward_head.units: 1024
xlargerew:
reward_head.layers: 6
reward_head.units: 1280
small:
rssm.deter: 512
.*\.cnn_depth: 32
.*\.units: 512
.*\.layers: 2
medium:
rssm.deter: 1024
.*\.cnn_depth: 48
.*\.units: 640
.*\.layers: 3
large:
rssm.deter: 2048
.*\.cnn_depth: 64
.*\.units: 768
.*\.layers: 4
xlarge:
rssm.deter: 4096
.*\.cnn_depth: 96
.*\.units: 1024
.*\.layers: 5
multicpu:
jax:
logical_cpus: 8
policy_devices: [0, 1]
train_devices: [2, 3, 4, 5, 6, 7]
run:
actor_batch: 4
envs:
amount: 8
batch_size: 12
batch_length: 10
debug:
jax: {jit: True, prealloc: False, debug: True, platform: cpu, profiler: False}
envs: {restart: False, amount: 3}
wrapper: {length: 100, checks: True}
run:
eval_every: 1000
log_every: 5
save_every: 10
train_ratio: 32
actor_batch: 2
batch_size: 8
batch_length: 12
replay_size: 1e5
encoder.cnn_depth: 8
decoder.cnn_depth: 8
rssm: {deter: 32, units: 16, stoch: 4, classes: 4}
.*unroll: False
.*\.layers: 2
.*\.units: 16
.*\.wd$: 0.0