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from easydict import EasyDict
halfcheetah_trex_sac_config = dict(
exp_name='halfcheetah_trex_sac_seed0',
env=dict(
env_id='HalfCheetah-v3',
norm_obs=dict(use_norm=False, ),
norm_reward=dict(use_norm=False, ),
collector_env_num=1,
evaluator_env_num=8,
n_evaluator_episode=8,
stop_value=12000,
),
reward_model=dict(
learning_rate=1e-5,
min_snippet_length=30,
max_snippet_length=100,
checkpoint_min=1000,
checkpoint_max=9000,
checkpoint_step=1000,
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``.
# However, here in ``expert_model_path``, it is ``exp_name`` of the expert config.
expert_model_path='model_path_placeholder',
# Path where to store the reward model
reward_model_path='data_path_placeholder + /HalfCheetah.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
# See ding/entry/application_entry_trex_collect_data.py to collect the data
data_path='data_path_placeholder',
),
policy=dict(
cuda=True,
random_collect_size=10000,
model=dict(
obs_shape=17,
action_shape=6,
twin_critic=True,
action_space='reparameterization',
actor_head_hidden_size=256,
critic_head_hidden_size=256,
),
learn=dict(
update_per_collect=1,
batch_size=256,
learning_rate_q=1e-3,
learning_rate_policy=1e-3,
learning_rate_alpha=3e-4,
ignore_done=True,
target_theta=0.005,
discount_factor=0.99,
alpha=0.2,
reparameterization=True,
auto_alpha=False,
),
collect=dict(
n_sample=1,
unroll_len=1,
),
command=dict(),
eval=dict(),
other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ),
),
)
halfcheetah_trex_sac_config = EasyDict(halfcheetah_trex_sac_config)
main_config = halfcheetah_trex_sac_config
halfcheetah_trex_sac_create_config = dict(
env=dict(
type='mujoco',
import_names=['dizoo.mujoco.envs.mujoco_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(
type='sac',
import_names=['ding.policy.sac'],
),
replay_buffer=dict(type='naive', ),
reward_model=dict(type='trex'),
)
halfcheetah_trex_sac_create_config = EasyDict(halfcheetah_trex_sac_create_config)
create_config = halfcheetah_trex_sac_create_config
if __name__ == '__main__':
# Users should first run ``halfcheetah_sac_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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