import torch from copy import deepcopy from ding.entry import serial_pipeline_offline, collect_demo_data, eval, serial_pipeline def train_cql(args): from dizoo.classic_control.cartpole.config.cartpole_cql_config import main_config, create_config main_config.exp_name = 'cartpole_cql' main_config.policy.collect.data_path = './cartpole/expert_demos.hdf5' main_config.policy.collect.data_type = 'hdf5' config = deepcopy([main_config, create_config]) serial_pipeline_offline(config, seed=args.seed) def eval_ckpt(args): from dizoo.classic_control.cartpole.config.cartpole_qrdqn_config import main_config, create_config main_config, create_config = deepcopy(main_config), deepcopy(create_config) main_config.exp_name = 'cartpole' config = deepcopy([main_config, create_config]) eval(config, seed=args.seed, load_path='./cartpole/ckpt/ckpt_best.pth.tar') def generate(args): from dizoo.classic_control.cartpole.config.cartpole_qrdqn_generation_data_config import main_config, create_config main_config.exp_name = 'cartpole' main_config.policy.collect.save_path = './cartpole/expert.pkl' main_config.policy.collect.data_type = 'hdf5' config = deepcopy([main_config, create_config]) state_dict = torch.load('./cartpole/ckpt/ckpt_best.pth.tar', map_location='cpu') collect_demo_data( config, collect_count=10000, seed=args.seed, expert_data_path=main_config.policy.collect.save_path, state_dict=state_dict ) def train_expert(args): from dizoo.classic_control.cartpole.config.cartpole_qrdqn_config import main_config, create_config main_config, create_config = deepcopy(main_config), deepcopy(create_config) main_config.exp_name = 'cartpole' config = deepcopy([main_config, create_config]) serial_pipeline(config, seed=args.seed) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser() parser.add_argument('--seed', '-s', type=int, default=10) args = parser.parse_args() train_expert(args) eval_ckpt(args) generate(args) train_cql(args)