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from easydict import EasyDict
lunarlander_sqil_config = dict(
exp_name='lunarlander_sqil_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=False,
model=dict(
obs_shape=8,
action_shape=4,
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=0.08),
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 "unrol_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_sqil_config = EasyDict(lunarlander_sqil_config)
main_config = lunarlander_sqil_config
lunarlander_sqil_create_config = dict(
env=dict(
type='lunarlander',
import_names=['dizoo.box2d.lunarlander.envs.lunarlander_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='sql'),
)
lunarlander_sqil_create_config = EasyDict(lunarlander_sqil_create_config)
create_config = lunarlander_sqil_create_config
if __name__ == '__main__':
# or you can enter `ding -m serial_sqil -c lunarlander_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.box2d.lunarlander.config import lunarlander_dqn_config, lunarlander_dqn_create_config
expert_main_config = lunarlander_dqn_config
expert_create_config = lunarlander_dqn_create_config
serial_pipeline_sqil([main_config, create_config], [expert_main_config, expert_create_config], seed=0) |