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from easydict import EasyDict
bipedalwalker_a2c_config = dict(
exp_name='bipedalwalker_a2c_seed0',
env=dict(
env_id='BipedalWalker-v3',
collector_env_num=8,
evaluator_env_num=8,
# (bool) Scale output action into legal range.
act_scale=True,
n_evaluator_episode=8,
stop_value=300,
rew_clip=True,
# The path to save the game replay
# replay_path='./bipedalwalker_a2c_seed0/video',
),
policy=dict(
cuda=True,
# load_path="./bipedalwalker_a2c_seed0/ckpt/ckpt_best.pth.tar",
action_space='continuous',
model=dict(
action_space='continuous',
obs_shape=24,
action_shape=4,
),
learn=dict(
# (int) the number of data for a train iteration
batch_size=256,
learning_rate=0.0003,
# (float) loss weight of the value network, the weight of policy network is set to 1
value_weight=0.5,
# (float) loss weight of the entropy regularization, the weight of policy network is set to 1
entropy_weight=0.001,
# (float) discount factor for future reward, defaults int [0, 1]
discount_factor=0.99,
adv_norm=True,
),
collect=dict(
# (int) collect n_sample data, train model n_iteration times
n_sample=512,
discount_factor=0.99,
collector=dict(collect_print_freq=100, ),
),
eval=dict(evaluator=dict(eval_freq=100, )),
),
)
bipedalwalker_a2c_config = EasyDict(bipedalwalker_a2c_config)
main_config = bipedalwalker_a2c_config
bipedalwalker_a2c_create_config = dict(
env=dict(
type='bipedalwalker',
import_names=['dizoo.box2d.bipedalwalker.envs.bipedalwalker_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='a2c'),
replay_buffer=dict(type='naive'),
)
bipedalwalker_a2c_create_config = EasyDict(bipedalwalker_a2c_create_config)
create_config = bipedalwalker_a2c_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial_onpolicy -c bipedalwalker_a2c_config.py -s 0`
from ding.entry import serial_pipeline_onpolicy
serial_pipeline_onpolicy([main_config, create_config], seed=0)
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