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from easydict import EasyDict
lunarlander_dqfd_config = dict(
exp_name='lunarlander_dqfd_seed0',
env=dict(
# Whether to use shared memory. Only effective if "env_manager_type" is 'subprocess'
collector_env_num=8,
evaluator_env_num=8,
env_id='LunarLander-v2',
n_evaluator_episode=8,
stop_value=200,
),
policy=dict(
cuda=True,
model=dict(
obs_shape=8,
action_shape=4,
encoder_hidden_size_list=[512, 64],
dueling=True,
),
nstep=3,
discount_factor=0.97,
learn=dict(
batch_size=64,
learning_rate=0.001,
lambda1=1.0,
lambda2=1.0,
lambda3=1e-5,
per_train_iter_k=10,
expert_replay_buffer_size=10000, # justify the buffer size of the expert buffer
),
collect=dict(
n_sample=64,
# Users should add their own model path here. Model path should lead to a model.
# Absolute path is recommended.
# In DI-engine, it is ``exp_name/ckpt/ckpt_best.pth.tar``.
model_path='model_path_placeholder',
# Cut trajectories into pieces with length "unroll_len".
unroll_len=1,
),
eval=dict(evaluator=dict(eval_freq=50, )), # note: this is the times after which you learns to evaluate
other=dict(
eps=dict(
type='exp',
start=0.95,
end=0.1,
decay=10000,
),
replay_buffer=dict(replay_buffer_size=20000, ),
),
),
)
lunarlander_dqfd_config = EasyDict(lunarlander_dqfd_config)
main_config = lunarlander_dqfd_config
lunarlander_dqfd_create_config = dict(
env=dict(
type='lunarlander',
import_names=['dizoo.box2d.lunarlander.envs.lunarlander_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='dqfd'),
)
lunarlander_dqfd_create_config = EasyDict(lunarlander_dqfd_create_config)
create_config = lunarlander_dqfd_create_config
if __name__ == '__main__':
# or you can enter `ding -m serial_dqfd -c lunarlander_dqfd_config.py -s 0`
# then input ``lunarlander_dqfd_config.py`` upon the instructions.
# The reason we need to input the dqfd config is we have to borrow its ``_get_train_sample`` function
# in the collector part even though the expert model may be generated from other Q learning algos.
from ding.entry.serial_entry_dqfd import serial_pipeline_dqfd
from dizoo.box2d.lunarlander.config import lunarlander_dqfd_config, lunarlander_dqfd_create_config
expert_main_config = lunarlander_dqfd_config
expert_create_config = lunarlander_dqfd_create_config
serial_pipeline_dqfd([main_config, create_config], [expert_main_config, expert_create_config], seed=0)
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