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from easydict import EasyDict
nstep = 3
lunarlander_dqn_config = dict(
exp_name='lunarlander_dqn_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,
# The path to save the game replay
# replay_path='./lunarlander_dqn_seed0/video',
),
policy=dict(
# Whether to use cuda for network.
cuda=True,
load_path="./lunarlander_dqn_seed0/ckpt/ckpt_best.pth.tar",
model=dict(
obs_shape=8,
action_shape=4,
encoder_hidden_size_list=[512, 64],
# Whether to use dueling head.
dueling=True,
),
# Reward's future discount factor, aka. gamma.
discount_factor=0.99,
# How many steps in td error.
nstep=nstep,
# learn_mode config
learn=dict(
update_per_collect=10,
batch_size=64,
learning_rate=0.001,
# Frequency of target network update.
target_update_freq=100,
),
# collect_mode config
collect=dict(
# You can use either "n_sample" or "n_episode" in collector.collect.
# Get "n_sample" samples per collect.
n_sample=64,
# Cut trajectories into pieces with length "unroll_len".
unroll_len=1,
),
# command_mode config
other=dict(
# Epsilon greedy with decay.
eps=dict(
# Decay type. Support ['exp', 'linear'].
type='exp',
start=0.95,
end=0.1,
decay=50000,
),
replay_buffer=dict(replay_buffer_size=100000, )
),
),
)
lunarlander_dqn_config = EasyDict(lunarlander_dqn_config)
main_config = lunarlander_dqn_config
lunarlander_dqn_create_config = dict(
env=dict(
type='lunarlander',
import_names=['dizoo.box2d.lunarlander.envs.lunarlander_env'],
),
env_manager=dict(type='subprocess'),
# env_manager=dict(type='base'),
policy=dict(type='dqn'),
)
lunarlander_dqn_create_config = EasyDict(lunarlander_dqn_create_config)
create_config = lunarlander_dqn_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial -c lunarlander_dqn_config.py -s 0`
from ding.entry import serial_pipeline
serial_pipeline([main_config, create_config], seed=0)
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