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actent: 0.0003
actor:
act: silu
fan: avg
inputs: [deter, stoch]
layers: 2
maxstd: 1.0
minstd: 0.1
norm: layer
outnorm: false
outscale: 1.0
symlog_inputs: false
unimix: 0.01
units: 512
winit: normal
actor_dist_cont: normal
actor_dist_disc: onehot
actor_grad_cont: backprop
actor_grad_disc: reinforce
actor_opt: {clip: 100.0, eps: 1e-05, lateclip: 0.0, lr: 3e-05, opt: adam, warmup: 0,
wd: 0.0}
batch_length: 64
batch_size: 16
cont_head:
act: silu
dist: binary
fan: avg
inputs: [deter, stoch]
layers: 2
norm: layer
outnorm: false
outscale: 1.0
units: 512
winit: normal
critic:
act: silu
bins: 255
dist: symlog_disc
fan: avg
inputs: [deter, stoch]
layers: 2
norm: layer
outnorm: false
outscale: 0.0
symlog_inputs: false
units: 512
winit: normal
critic_opt: {clip: 100.0, eps: 1e-05, lateclip: 0.0, lr: 3e-05, opt: adam, warmup: 0,
wd: 0.0}
critic_slowreg: logprob
critic_type: vfunction
data_loaders: 8
decoder:
act: silu
cnn: resnet
cnn_blocks: 0
cnn_depth: 32
cnn_keys: image
cnn_sigmoid: false
fan: avg
image_dist: mse
inputs: [deter, stoch]
minres: 4
mlp_keys: $^
mlp_layers: 5
mlp_units: 1024
norm: layer
outscale: 1.0
resize: stride
vector_dist: symlog_mse
winit: normal
disag_head:
act: silu
dist: mse
fan: avg
inputs: [deter, stoch, action]
layers: 2
norm: layer
outscale: 1.0
units: 512
winit: normal
disag_models: 8
disag_target: [stoch]
dyn_loss: {free: 1.0, impl: kl}
encoder: {act: silu, cnn: resnet, cnn_blocks: 0, cnn_depth: 32, cnn_keys: image, fan: avg,
minres: 4, mlp_keys: $^, mlp_layers: 5, mlp_units: 1024, norm: layer, resize: stride,
symlog_inputs: true, winit: normal}
env:
atari:
actions: all
gray: false
lives: unused
noops: 0
repeat: 4
resize: opencv
size: [64, 64]
sticky: true
dmc:
camera: -1
repeat: 2
size: [64, 64]
dmlab:
episodic: true
repeat: 4
size: [64, 64]
loconav:
camera: -1
repeat: 2
size: [64, 64]
minecraft:
break_speed: 100.0
size: [64, 64]
envs: {amount: 4, checks: false, discretize: 0, length: 0, parallel: process, reset: true,
restart: true}
eval_dir: ''
expl_behavior: None
expl_opt: {clip: 100.0, eps: 1e-05, lr: 0.0001, opt: adam, warmup: 0, wd: 0.0}
expl_rewards: {disag: 0.1, extr: 1.0}
filter: .*
grad_heads: [decoder, reward, cont]
horizon: 333
imag_horizon: 15
imag_unroll: false
jax:
debug: false
debug_nans: false
jit: true
logical_cpus: 0
metrics_every: 10
platform: gpu
policy_devices: [1]
prealloc: true
precision: float16
train_devices: [1]
logdir: ./logdir/dmc_cartpole_balance
loss_scales: {actor: 1.0, cont: 1.0, critic: 1.0, dyn: 0.5, image: 1.0, rep: 0.1,
reward: 1.0, slowreg: 1.0, vector: 1.0}
method: name
model_opt: {clip: 1000.0, eps: 1e-08, lateclip: 0.0, lr: 0.0001, opt: adam, warmup: 0,
wd: 0.0}
rep_loss: {free: 1.0, impl: kl}
replay: uniform
replay_online: false
replay_size: 1000000.0
retnorm: {decay: 0.99, impl: perc_ema, max: 1.0, perchi: 95.0, perclo: 5.0}
return_lambda: 0.95
reward_head:
act: silu
bins: 255
dist: symlog_disc
fan: avg
inputs: [deter, stoch]
layers: 2
norm: layer
outnorm: false
outscale: 0.0
units: 512
winit: normal
rssm: {act: silu, action_clip: 1.0, classes: 32, deter: 512, fan: avg, initial: learned,
norm: layer, stoch: 32, unimix: 0.01, units: 512, unroll: false, winit: normal}
run:
actor_addr: ipc:///tmp/5551
actor_batch: 32
eval_eps: 1
eval_every: 1000000.0
eval_fill: 0
eval_initial: true
eval_samples: 1
expl_until: 0
from_checkpoint: ''
log_every: 300
log_keys_max: ^$
log_keys_mean: (log_entropy)
log_keys_sum: ^$
log_keys_video: [image]
log_zeros: false
save_every: 900
script: train
steps: 10000000000.0
sync_every: 10
train_fill: 0
train_ratio: 512.0
seed: 0
slow_critic_fraction: 0.02
slow_critic_update: 1
task: dmc_cartpole_balance
task_behavior: Greedy
wrapper: {checks: false, discretize: 0, length: 0, reset: true}
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