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from easydict import EasyDict
cartpole_sqil_config = dict(
exp_name='cartpole_sqil_seed0',
env=dict(
collector_env_num=8,
evaluator_env_num=5,
n_evaluator_episode=5,
stop_value=195,
),
policy=dict(
cuda=False,
model=dict(
obs_shape=4,
action_shape=2,
encoder_hidden_size_list=[128, 128, 64],
dueling=True,
),
nstep=1,
discount_factor=0.97,
learn=dict(batch_size=64, learning_rate=0.001, alpha=0.12),
collect=dict(
n_sample=8,
# 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='cartpole_dqn_seed0/ckpt/eval.pth.tar'
),
# note: this is the times after which you learns to evaluate
eval=dict(evaluator=dict(eval_freq=50, )),
other=dict(
eps=dict(
type='exp',
start=0.95,
end=0.1,
decay=10000,
),
replay_buffer=dict(replay_buffer_size=20000, ),
),
),
)
cartpole_sqil_config = EasyDict(cartpole_sqil_config)
main_config = cartpole_sqil_config
cartpole_sqil_create_config = dict(
env=dict(
type='cartpole',
import_names=['dizoo.classic_control.cartpole.envs.cartpole_env'],
),
env_manager=dict(type='base'),
policy=dict(type='sql'),
)
cartpole_sqil_create_config = EasyDict(cartpole_sqil_create_config)
create_config = cartpole_sqil_create_config
if __name__ == '__main__':
# or you can enter `ding -m serial_sqil -c cartpole_sqil_config.py -s 0`
# then input the config you used to generate your expert model in the path mentioned above
# e.g. spaceinvaders_dqn_config.py
from ding.entry import serial_pipeline_sqil
from dizoo.classic_control.cartpole.config import cartpole_dqn_config, cartpole_dqn_create_config
expert_main_config = cartpole_dqn_config
expert_create_config = cartpole_dqn_create_config
serial_pipeline_sqil((main_config, create_config), (expert_main_config, expert_create_config), seed=0)
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