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from easydict import EasyDict
gym_hybrid_mpdqn_config = dict(
exp_name='gym_hybrid_mpdqn_seed0',
env=dict(
collector_env_num=8,
evaluator_env_num=5,
# (bool) Scale output action into legal range [-1, 1].
act_scale=True,
env_id='Moving-v0', # ['Sliding-v0', 'Moving-v0']
n_evaluator_episode=5,
stop_value=1.8,
),
policy=dict(
cuda=True,
discount_factor=0.99,
nstep=1,
model=dict(
obs_shape=10,
action_shape=dict(
action_type_shape=3,
action_args_shape=2,
),
multi_pass=True,
action_mask=[[1, 0], [0, 1], [0, 0]],
),
learn=dict(
update_per_collect=500, # 10~500
batch_size=320,
learning_rate_dis=3e-4,
learning_rate_cont=3e-4,
target_theta=0.001,
update_circle=10,
),
# collect_mode config
collect=dict(
# (int) Only one of [n_sample, n_episode] shoule be set
n_sample=3200,
# (int) Cut trajectories into pieces with length "unroll_len".
unroll_len=1,
noise_sigma=0.1,
collector=dict(collect_print_freq=1000, ),
),
eval=dict(evaluator=dict(eval_freq=1000, ), ),
# other config
other=dict(
# Epsilon greedy with decay.
eps=dict(
# (str) Decay type. Support ['exp', 'linear'].
type='exp',
start=1,
end=0.1,
# (int) Decay length(env step)
decay=int(1e5),
),
replay_buffer=dict(replay_buffer_size=int(1e6), ),
),
)
)
gym_hybrid_mpdqn_config = EasyDict(gym_hybrid_mpdqn_config)
main_config = gym_hybrid_mpdqn_config
gym_hybrid_mpdqn_create_config = dict(
env=dict(
type='gym_hybrid',
import_names=['dizoo.gym_hybrid.envs.gym_hybrid_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='pdqn'),
)
gym_hybrid_mpdqn_create_config = EasyDict(gym_hybrid_mpdqn_create_config)
create_config = gym_hybrid_mpdqn_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial -c gym_hybrid_mpdqn_config.py -s 0`
from ding.entry import serial_pipeline
serial_pipeline([main_config, create_config], seed=0, max_env_step=int(1e7))
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