Source code for openrl.runners.common.dqn_agent

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2023 The OpenRL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""""""
from typing import Dict, Optional, Tuple, Union

import gym
import numpy as np
import torch

from openrl.algorithms.dqn import DQNAlgorithm as TrainAlgo
from openrl.buffers import OffPolicyReplayBuffer as ReplayBuffer
from openrl.buffers.utils.obs_data import ObsData
from openrl.drivers.offpolicy_driver import OffPolicyDriver as Driver
from openrl.runners.common.base_agent import SelfAgent
from openrl.runners.common.rl_agent import RLAgent
from openrl.utils.logger import Logger
from openrl.utils.util import _t2n


class DQNAgent(RLAgent):
    def __init__(
        self,
        net: Optional[torch.nn.Module] = None,
        env: Union[gym.Env, str] = None,
        run_dir: Optional[str] = None,
        env_num: Optional[int] = None,
        rank: int = 0,
        world_size: int = 1,
        use_wandb: bool = False,
        use_tensorboard: bool = False,
    ) -> None:
        super(DQNAgent, self).__init__(
            net, env, run_dir, env_num, rank, world_size, use_wandb, use_tensorboard
        )

    def train(self: SelfAgent, total_time_steps: int) -> None:
        self._cfg.num_env_steps = total_time_steps

        self.config = {
            "cfg": self._cfg,
            "num_agents": self.agent_num,
            "run_dir": self.run_dir,
            "envs": self._env,
            "device": self.net.device,
        }

        trainer = TrainAlgo(
            cfg=self._cfg,
            init_module=self.net.module,
            device=self.net.device,
            agent_num=self.agent_num,
        )

        buffer = ReplayBuffer(
            self._cfg,
            self.agent_num,
            self._env.observation_space,
            self._env.action_space,
            data_client=None,
            episode_length=self._cfg.episode_length,
        )

        logger = Logger(
            cfg=self._cfg,
            project_name="DQNAgent",
            scenario_name=self._env.env_name,
            wandb_entity=self._cfg.wandb_entity,
            exp_name=self.exp_name,
            log_path=self.run_dir,
            use_wandb=self._use_wandb,
            use_tensorboard=self._use_tensorboard,
        )
        driver = Driver(
            config=self.config,
            trainer=trainer,
            buffer=buffer,
            agent=self,
            client=self.client,
            rank=self.rank,
            world_size=self.world_size,
            logger=logger,
        )
        driver.run()

    def act(
        self, observation: Union[np.ndarray, Dict[str, np.ndarray]]
    ) -> Tuple[np.ndarray, Optional[Tuple[np.ndarray, ...]]]:
        assert self.net is not None, "net is None"
        observation = ObsData.prepare_input(observation)
        action, rnn_state = self.net.act(observation)

        action = np.array(np.split(action, self.env_num))

        return action, rnn_state