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from easydict import EasyDict
collector_env_num = 8
evaluator_env_num = 8
main_config = dict(
exp_name='HAPPO_result/multi_mujoco_halfcheetah_2x3_mappo',
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,
action_space='continuous',
model=dict(
# (int) agent_num: The number of the agent.
# For SMAC 3s5z, agent_num=8; for 2c_vs_64zg, agent_num=2.
agent_num=2,
# (int) obs_shape: The shapeension of observation of each agent.
# For 3s5z, obs_shape=150; for 2c_vs_64zg, agent_num=404.
# (int) global_obs_shape: The shapeension of global observation.
# For 3s5z, obs_shape=216; for 2c_vs_64zg, agent_num=342.
agent_obs_shape=8,
#global_obs_shape=216,
global_obs_shape=17,
# (int) action_shape: The number of action which each agent can take.
# action_shape= the number of common action (6) + the number of enemies.
# For 3s5z, obs_shape=14 (6+8); for 2c_vs_64zg, agent_num=70 (6+64).
action_shape=3,
# (List[int]) The size of hidden layer
# hidden_size_list=[64],
action_space='continuous'
),
# used in state_num of hidden_state
learn=dict(
epoch_per_collect=5,
batch_size=800,
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,
# (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=5,
),
collect=dict(env_num=collector_env_num, n_sample=3200),
eval=dict(env_num=evaluator_env_num, evaluator=dict(eval_freq=1000, )),
),
)
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='subprocess'),
policy=dict(type='ppo'),
)
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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