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from easydict import EasyDict
collector_env_num = 8
evaluator_env_num = 5
minigrid_icm_onppo_config = dict(
exp_name='minigrid_AKTDT-7x7_icm_onppo_seed0',
env=dict(
collector_env_num=collector_env_num,
evaluator_env_num=evaluator_env_num,
n_evaluator_episode=evaluator_env_num,
# minigrid env id: 'MiniGrid-Empty-8x8-v0', 'MiniGrid-FourRooms-v0','MiniGrid-DoorKey-16x16-v0','MiniGrid-AKTDT-7x7-1-v0'
env_id='MiniGrid-NoisyTV-v0',
max_step=100,
stop_value=12, # run fixed env_steps for MiniGrid-AKTDT-7x7-1-v0
# stop_value=0.96,
),
reward_model=dict(
intrinsic_reward_type='add',
# intrinsic_reward_weight means the relative weight of ICM intrinsic_reward.
# Specifically for sparse reward env MiniGrid, in this env,
# if reach goal, the agent get reward ~1, otherwise 0,
# We could set the intrinsic_reward_weight approximately equal to the inverse of max_episode_steps.
# Please refer to rnd_reward_model for details.
intrinsic_reward_weight=0.003, # 1/300
learning_rate=3e-4,
obs_shape=2835, # 2715 in MiniGrid-AKTDT-7x7-1-v0 env
batch_size=320,
update_per_collect=50,
clear_buffer_per_iters=int(1e3),
extrinsic_reward_norm=True,
extrinsic_reward_norm_max=1,
),
policy=dict(
cuda=True,
recompute_adv=True,
action_space='discrete',
model=dict(
obs_shape=2835, # 2715 in MiniGrid-AKTDT-7x7-1-v0 env
action_shape=7,
action_space='discrete',
encoder_hidden_size_list=[256, 128, 64, 64],
critic_head_hidden_size=64,
actor_head_hidden_size=64,
),
learn=dict(
epoch_per_collect=10,
update_per_collect=1,
batch_size=320,
learning_rate=3e-4,
value_weight=0.5,
entropy_weight=0.001,
clip_ratio=0.2,
adv_norm=True,
value_norm=True,
),
collect=dict(
n_sample=3200,
unroll_len=1,
discount_factor=0.99,
gae_lambda=0.95,
),
eval=dict(evaluator=dict(eval_freq=1000, )),
),
)
minigrid_icm_onppo_config = EasyDict(minigrid_icm_onppo_config)
main_config = minigrid_icm_onppo_config
minigrid_icm_onppo_create_config = dict(
env=dict(
type='minigrid',
import_names=['dizoo.minigrid.envs.minigrid_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='ppo'),
reward_model=dict(type='icm'),
)
minigrid_icm_onppo_create_config = EasyDict(minigrid_icm_onppo_create_config)
create_config = minigrid_icm_onppo_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial -c minigrid_icm_onppo_config.py -s 0`
from ding.entry import serial_pipeline_reward_model_onpolicy
serial_pipeline_reward_model_onpolicy([main_config, create_config], seed=0, max_env_step=int(10e6)) |