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| import os | |
| import torch | |
| from tensorboardX import SummaryWriter | |
| from ding.config import compile_config | |
| from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer | |
| from ding.policy import FQFPolicy | |
| from ding.model import FQF | |
| from ding.utils import set_pkg_seed | |
| from ding.rl_utils import get_epsilon_greedy_fn | |
| from dizoo.atari.config.serial.pong.pong_fqf_config import pong_fqf_config, create_config | |
| from ding.utils import DistContext | |
| from functools import partial | |
| from ding.envs import get_vec_env_setting, create_env_manager | |
| def main(cfg, create_cfg, seed=0): | |
| cfg = compile_config(cfg, seed=seed, env=None, auto=True, create_cfg=create_cfg, save_cfg=True) | |
| # Create main components: env, policy | |
| env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env) | |
| collector_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in collector_env_cfg]) | |
| evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg]) | |
| # Set random seed for all package and instance | |
| collector_env.seed(seed) | |
| evaluator_env.seed(seed, dynamic_seed=False) | |
| set_pkg_seed(seed, use_cuda=cfg.policy.cuda) | |
| # Set up RL Policy | |
| model = FQF(**cfg.policy.model) | |
| policy = FQFPolicy(cfg.policy, model=model) | |
| # Set up collection, training and evaluation utilities | |
| tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial')) | |
| learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) | |
| collector = SampleSerialCollector( | |
| cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name | |
| ) | |
| evaluator = InteractionSerialEvaluator( | |
| cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name | |
| ) | |
| replay_buffer = AdvancedReplayBuffer(cfg.policy.other.replay_buffer, tb_logger, exp_name=cfg.exp_name) | |
| # Set up other modules, etc. epsilon greedy | |
| eps_cfg = cfg.policy.other.eps | |
| epsilon_greedy = get_epsilon_greedy_fn(eps_cfg.start, eps_cfg.end, eps_cfg.decay, eps_cfg.type) | |
| # Training & Evaluation loop | |
| while True: | |
| # Evaluating at the beginning and with specific frequency | |
| if evaluator.should_eval(learner.train_iter): | |
| stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep) | |
| if stop: | |
| break | |
| # Update other modules | |
| eps = epsilon_greedy(collector.envstep) | |
| # Sampling data from environments | |
| new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs={'eps': eps}) | |
| replay_buffer.push(new_data, cur_collector_envstep=collector.envstep) | |
| # Training | |
| for i in range(cfg.policy.learn.update_per_collect): | |
| train_data = replay_buffer.sample(learner.policy.get_attribute('batch_size'), learner.train_iter) | |
| if train_data is None: | |
| break | |
| learner.train(train_data, collector.envstep) | |
| if collector.envstep >= 10000000: | |
| break | |
| if __name__ == "__main__": | |
| # with DistContext(): | |
| main(pong_fqf_config, create_config) | |