def get_task_config():
wann_param_config = task.get_default_wann_hyperparams()
wann_param_config['task'] = ENV_NAME
wann_param_config['maxGen'] = 1
wann_param_config['popSize'] = 100
wann_param_config['alg_nReps'] = 1
task_config = dict(
WANN_ENV_ID='wann-humanoid-v0',
NUM_WORKERS=5,
DEVICE='cuda:0',
GAME_CONFIG=task.Game(env_name=ENV_NAME,
actionSelect='all', # OPTIONS: soft, all, hard
input_size=44,
output_size=44,
time_factor=0,
layers=[40, 40],
i_act=np.full(44, 1),
h_act=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
o_act=np.full(44, 1),
weightCap=2.0,
noise_bias=0.0,
output_noise=[False, False, False],
max_episode_length=1000,
n_critic_bootstrap=5,
alg_type=task.ALG.SAC,
artifacts_path=f'{task.RESULTS_PATH}artifact{os.sep}{config.EXPERIMENT_ID}{os.sep}',
in_out_labels=[]),
AGENT=dict(
datprep=None,
policy=MlpPolicy,
mini_mem_size=int(1e6//10),
wann_batch_size=1024,
wann_bootstrap_default=-100,
learn_params=dict(
gamma=0.99,
tau=5e-3,
learn_rate=1e-4,
mem_size=int(1e6),
target_entropy='auto',
timesteps=1000,
train_batch_size=100, # batch buffer size
episode_len=-1, # entire episode length
eval_episode_len=-1,
alg_checkpoint_interval=500,
start_steps=100,
n_trains_per_step=1, # soft target updates should use 1, try 5 for hard target updates
gradient_steps_per_step=1,
eval_interval=1500,
log_interval=10,
log_verbose=1,
replay_sample_ratio=1, # 4:1 or .25 replay buffer sample to gradient update ratio
)
),
WANN_PARAM_CONFIG=wann_param_config,
VIDEO_LENGTH=1500,
RESULTS_PATH=task.RESULTS_PATH
)
return copy.deepcopy(task_config)
def get_task_config():
wann_param_config = task.get_default_wann_hyperparams()
wann_param_config['task'] = ENV_NAME
wann_param_config['maxGen'] = 1
wann_param_config['popSize'] = 20
wann_param_config['alg_nReps'] = 1
task_config = dict(
WANN_ENV_ID='wann-cartpole',
NUM_WORKERS=5, # mp.cpu_count() for all cores
GAME_CONFIG=task.Game(
env_name='CartPole-v1',
actionSelect='all', # all, soft, hard
input_size=4, # greyscale + 4 frame stacking
output_size=4,
time_factor=0,
layers=[25, 25],
i_act=np.full(4, 1),
h_act=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
o_act=np.full(4, 1),
weightCap=2.0,
noise_bias=0.0,
output_noise=[False, False, False],
max_episode_length=
300, # use full episode length or reasonable trajectory len here
n_critic_bootstrap=5,
alg_type=task.ALG.SAC,
artifacts_path=
f'{task.RESULTS_PATH}artifact{os.sep}{config.EXPERIMENT_ID}{os.sep}',
in_out_labels=[]),
AGENT=dict(
datprep=None,
policy=MlpPolicy,
mini_mem_size=int(1e6 // 10),
wann_batch_size=1024,
wann_bootstrap_default=-100,
learn_params=dict(
gamma=0.99,
tau=5e-3,
learn_rate=1e-4,
mem_size=int(1e6),
target_entropy='auto',
timesteps=
300, # for baseline SAC give enough timesteps to be at least 500 episodes
train_batch_size=100, # batch buffer size
episode_len=-1, # entire episode length
eval_episode_len=-1,
alg_checkpoint_interval=500,
start_steps=10,
n_trains_per_step=
1, # soft target updates should use 1, try 5 for hard target updates
gradient_steps_per_step=1,
eval_interval=1500,
log_interval=10,
log_verbose=1,
replay_sample_ratio=1, # 1:1 or 4:1 for 1e6 replay mem size
)),
WANN_PARAM_CONFIG=wann_param_config,
VIDEO_LENGTH=1500,
RESULTS_PATH=task.RESULTS_PATH)
return copy.deepcopy(task_config)