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from easydict import EasyDict
nstep = 1
lunarlander_trex_dqn_config = dict(
exp_name='lunarlander_trex_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,
),
reward_model=dict(
type='trex',
min_snippet_length=30,
max_snippet_length=100,
checkpoint_min=1000,
checkpoint_max=9000,
checkpoint_step=1000,
num_snippets=60000,
learning_rate=1e-5,
update_per_collect=1,
# 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``.
expert_model_path='model_path_placeholder',
# Path where to store the reward model
reward_model_path='data_path_placeholder + /lunarlander.params',
# Users should add their own data path here. Data path should lead to a file to store data or load the stored data.
# Absolute path is recommended.
# In DI-engine, it is usually located in ``exp_name`` directory
# e.g. 'exp_name/expert_data.pkl'
data_path='data_path_placeholder',
),
policy=dict(
# Whether to use cuda for network.
cuda=False,
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_trex_dqn_config = EasyDict(lunarlander_trex_dqn_config)
main_config = lunarlander_trex_dqn_config
lunarlander_trex_dqn_create_config = dict(
env=dict(
type='lunarlander',
import_names=['dizoo.box2d.lunarlander.envs.lunarlander_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='dqn'),
)
lunarlander_trex_dqn_create_config = EasyDict(lunarlander_trex_dqn_create_config)
create_config = lunarlander_trex_dqn_create_config
if __name__ == '__main__':
# Users should first run ``lunarlander_dqn_config.py`` to save models (or checkpoints).
# Note: Users should check that the checkpoints generated should include iteration_'checkpoint_min'.pth.tar, iteration_'checkpoint_max'.pth.tar with the interval checkpoint_step
# where checkpoint_max, checkpoint_min, checkpoint_step are specified above.
import argparse
import torch
from ding.entry import trex_collecting_data
from ding.entry import serial_pipeline_trex
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default='please enter abs path for this file')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu')
args = parser.parse_args()
# The function ``trex_collecting_data`` below is to collect episodic data for training the reward model in trex.
trex_collecting_data(args)
serial_pipeline_trex([main_config, create_config])
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