2019-12-20 10:51:25 -08:00
|
|
|
# Pendulum SAC can attain -150+ reward in 6-7k
|
|
|
|
|
# Configurations are the similar to original softlearning/sac codebase
|
|
|
|
|
pendulum_sac:
|
|
|
|
|
env: Pendulum-v0
|
|
|
|
|
run: SAC
|
2020-02-22 23:19:49 +01:00
|
|
|
stop:
|
2019-12-20 10:51:25 -08:00
|
|
|
episode_reward_mean: -150
|
|
|
|
|
config:
|
|
|
|
|
horizon: 200
|
|
|
|
|
soft_horizon: False
|
|
|
|
|
Q_model:
|
|
|
|
|
hidden_activation: relu
|
|
|
|
|
hidden_layer_sizes: [256, 256]
|
|
|
|
|
policy_model:
|
|
|
|
|
hidden_activation: relu
|
|
|
|
|
hidden_layer_sizes: [256, 256]
|
|
|
|
|
tau: 0.005
|
|
|
|
|
target_entropy: auto
|
|
|
|
|
no_done_at_end: True
|
|
|
|
|
n_step: 1
|
2020-03-14 12:05:04 -07:00
|
|
|
rollout_fragment_length: 1
|
2019-12-20 10:51:25 -08:00
|
|
|
prioritized_replay: False
|
|
|
|
|
train_batch_size: 256
|
|
|
|
|
target_network_update_freq: 1
|
|
|
|
|
timesteps_per_iteration: 1000
|
|
|
|
|
learning_starts: 256
|
|
|
|
|
optimization:
|
|
|
|
|
actor_learning_rate: 0.0003
|
|
|
|
|
critic_learning_rate: 0.0003
|
|
|
|
|
entropy_learning_rate: 0.0003
|
|
|
|
|
num_workers: 0
|
|
|
|
|
num_gpus: 0
|
|
|
|
|
clip_actions: False
|
|
|
|
|
normalize_actions: True
|
|
|
|
|
evaluation_interval: 1
|
|
|
|
|
metrics_smoothing_episodes: 5
|
