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from easydict import EasyDict
collector_env_num = 8
evaluator_env_num = 8
n_agent = 2
main_config = dict(
exp_name='HAPPO_result/debug/multi_mujoco_halfcheetah_2x3_happo',
env=dict(
scenario='HalfCheetah-v2',
agent_conf="2x3",
agent_obsk=2,
add_agent_id=False,
episode_limit=1000,
collector_env_num=collector_env_num,
evaluator_env_num=evaluator_env_num,
n_evaluator_episode=8,
stop_value=6000,
),
policy=dict(
cuda=True,
multi_agent=True,
agent_num=n_agent,
action_space='continuous',
model=dict(
action_space='continuous',
agent_num=n_agent,
agent_obs_shape=8,
global_obs_shape=17,
action_shape=3,
use_lstm=False,
),
learn=dict(
epoch_per_collect=5,
# batch_size=3200,
batch_size=800,
learning_rate=5e-4,
critic_learning_rate=5e-4,
# ==============================================================
# The following configs is algorithm-specific
# ==============================================================
# (float) The loss weight of value network, policy network weight is set to 1
value_weight=0.5,
# (float) The loss weight of entropy regularization, policy network weight is set to 1
# entropy_weight=0.001,
entropy_weight=0.001,
# (float) PPO clip ratio, defaults to 0.2
clip_ratio=0.2,
# (bool) Whether to use advantage norm in a whole training batch
adv_norm=True,
value_norm=True,
ppo_param_init=True,
grad_clip_type='clip_norm',
grad_clip_value=3,
ignore_done=True,
# ignore_done=False,
),
collect=dict(
n_sample=3200,
unroll_len=1,
env_num=collector_env_num,
),
eval=dict(
env_num=evaluator_env_num,
evaluator=dict(eval_freq=1000, ),
),
other=dict(),
),
)
main_config = EasyDict(main_config)
create_config = dict(
env=dict(
type='mujoco_multi',
import_names=['dizoo.multiagent_mujoco.envs.multi_mujoco_env'],
),
env_manager=dict(type='base'),
policy=dict(type='happo'),
)
create_config = EasyDict(create_config)
if __name__ == '__main__':
from ding.entry import serial_pipeline_onpolicy
serial_pipeline_onpolicy((main_config, create_config), seed=0, max_env_step=int(1e7))
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