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from easydict import EasyDict
hopper_bdq_config = dict(
exp_name='hopper_bdq_seed0',
env=dict(
env_id='Hopper-v3',
norm_reward=dict(use_norm=False, ),
collector_env_num=8,
evaluator_env_num=8,
n_evaluator_episode=8,
stop_value=int(1e6),
action_bins_per_branch=4,
),
policy=dict(
cuda=False,
priority=False,
discount_factor=0.99,
nstep=3,
model=dict(
obs_shape=11,
num_branches=3,
action_bins_per_branch=4, # mean the action shape is 3, 4 discrete actions for each action dimension
encoder_hidden_size_list=[256, 256, 128],
),
learn=dict(
ignore_done=False,
batch_size=512,
learning_rate=3e-4,
# Frequency of target network update.
target_update_freq=500,
update_per_collect=20,
),
collect=dict(
# You can use either "n_sample" or "n_episode" in collector.collect.
# Get "n_sample" samples per collect.
n_sample=256,
# Cut trajectories into pieces with length "unroll_len".
unroll_len=1,
),
eval=dict(evaluator=dict(eval_freq=1000, )),
other=dict(
# Epsilon greedy with decay.
eps=dict(
# Decay type. Support ['exp', 'linear'].
type='exp',
start=1,
end=0.05,
decay=int(1e5),
),
replay_buffer=dict(replay_buffer_size=int(1e6), )
),
),
)
hopper_bdq_config = EasyDict(hopper_bdq_config)
main_config = hopper_bdq_config
hopper_bdq_create_config = dict(
env=dict(
type='mujoco',
import_names=['dizoo.mujoco.envs.mujoco_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='bdq', ),
)
hopper_bdq_create_config = EasyDict(hopper_bdq_create_config)
create_config = hopper_bdq_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial_onpolicy -c hopper_bdq_config.py -s 0`
from ding.entry import serial_pipeline
serial_pipeline(
[main_config, create_config],
seed=0,
max_env_step=10000000,
)
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