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from easydict import EasyDict
nstep = 3
lunarlander_acer_config = dict(
exp_name='lunarlander_acer_seed0',
env=dict(
# Whether to use shared memory. Only effective if "env_manager_type" is 'subprocess'
# Env number respectively for collector and evaluator.
collector_env_num=8,
evaluator_env_num=8,
env_id='LunarLander-v2',
n_evaluator_episode=8,
stop_value=200,
),
policy=dict(
# Whether to use cuda for network.
cuda=False,
# Model config used for model creating. Remember to change this,
# especially "obs_shape" and "action_shape" according to specific env.
model=dict(
obs_shape=8,
action_shape=4,
encoder_hidden_size_list=[512, 64],
# Whether to use dueling head.
),
# Reward's future discount facotr, aka. gamma.
discount_factor=0.99,
# How many steps in td error.
nstep=nstep,
unroll_len=32,
# learn_mode config
learn=dict(
# (int) collect n_sample data, train model update_per_collect times
# here we follow impala serial pipeline
update_per_collect=10,
# (int) the number of data for a train iteration
batch_size=32,
# grad_clip_type='clip_norm',
# clip_value=10,
learning_rate_actor=0.0001,
learning_rate_critic=0.0001,
# (float) loss weight of the value network, the weight of policy network is set to 1
# (float) loss weight of the entropy regularization, the weight of policy network is set to 1
entropy_weight=0.0,
# (float) discount factor for future reward, defaults int [0, 1]
discount_factor=0.99,
# (float) additional discounting parameter
# (int) the trajectory length to calculate v-trace target
# (float) clip ratio of importance weights
c_clip_ratio=10,
),
collect=dict(
# (int) collect n_sample data, train model n_iteration times
n_sample=16,
# (float) discount factor for future reward, defaults int [0, 1]
discount_factor=0.99,
gae_lambda=0.95,
collector=dict(collect_print_freq=1000, ),
),
eval=dict(evaluator=dict(eval_freq=5000, )),
other=dict(replay_buffer=dict(replay_buffer_size=50000, ), ),
),
)
lunarlander_acer_config = EasyDict(lunarlander_acer_config)
main_config = lunarlander_acer_config
lunarlander_acer_create_config = dict(
env=dict(
type='lunarlander',
import_names=['dizoo.box2d.lunarlander.envs.lunarlander_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='acer'),
replay_buffer=dict(type='naive')
)
lunarlander_acer_create_config = EasyDict(lunarlander_acer_create_config)
create_config = lunarlander_acer_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial -c lunarlander_acer_config.py -s 0`
from ding.entry import serial_pipeline
serial_pipeline([main_config, create_config], seed=0)
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