dqn.py

"""
# Example of DQN pipeline

Use the pipeline on a single process:

> python3 -u ding/example/dqn.py

Use the pipeline on multiple processes:

We surpose there are N processes (workers) = 1 learner + 1 evaluator + (N-2) collectors

## First Example —— Execute on one machine with multi processes.

Execute 4 processes with 1 learner + 1 evaluator + 2 collectors
Remember to keep them connected by mesh to ensure that they can exchange information with each other.

> ditask --package . --main ding.example.dqn.main --parallel-workers 4 --topology mesh

## Second Example —— Execute on multiple machines.

1. Execute 1 learner + 1 evaluator on one machine.

> ditask --package . --main ding.example.dqn.main --parallel-workers 2 --topology mesh --node-ids 0 --ports 50515

2. Execute 2 collectors on another machine. (Suppose the ip of the first machine is 127.0.0.1).
    Here we use `alone` topology instead of `mesh` because the collectors do not need communicate with each other.
    Remember the `node_ids` cannot be duplicated with the learner, evaluator processes.
    And remember to set the `ports` (should not conflict with others) and `attach_to` parameters.
    The value of the `attach_to` parameter should be obtained from the log of the
    process started earlier (e.g. 'NNG listen on tcp://10.0.0.4:50515').

> ditask --package . --main ding.example.dqn.main --parallel-workers 2 --topology alone --node-ids 2 \
    --ports 50517 --attach-to tcp://10.0.0.4:50515,tcp://127.0.0.1:50516

3. You can repeat step 2 to start more collectors on other machines.
"""
import gym
from ditk import logging
from ding.data.model_loader import FileModelLoader
from ding.data.storage_loader import FileStorageLoader
from ding.model import DQN
from ding.policy import DQNPolicy
from ding.envs import DingEnvWrapper, BaseEnvManagerV2
from ding.data import DequeBuffer
from ding.config import compile_config
from ding.framework import task, ding_init
from ding.framework.context import OnlineRLContext
from ding.framework.middleware import OffPolicyLearner, StepCollector, interaction_evaluator, data_pusher, \
    eps_greedy_handler, CkptSaver, ContextExchanger, ModelExchanger, online_logger
from ding.utils import set_pkg_seed
from dizoo.classic_control.cartpole.config.cartpole_dqn_config import main_config, create_config


def main():
    logging.getLogger().setLevel(logging.INFO)
    cfg = compile_config(main_config, create_cfg=create_config, auto=True, save_cfg=task.router.node_id == 0)
    ding_init(cfg)
    with task.start(async_mode=False, ctx=OnlineRLContext()):
        collector_env = BaseEnvManagerV2(
            env_fn=[lambda: DingEnvWrapper(gym.make("CartPole-v0")) for _ in range(cfg.env.collector_env_num)],
            cfg=cfg.env.manager
        )
        evaluator_env = BaseEnvManagerV2(
            env_fn=[lambda: DingEnvWrapper(gym.make("CartPole-v0")) for _ in range(cfg.env.evaluator_env_num)],
            cfg=cfg.env.manager
        )

        set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)

        model = DQN(**cfg.policy.model)
        buffer_ = DequeBuffer(size=cfg.policy.other.replay_buffer.replay_buffer_size)
        policy = DQNPolicy(cfg.policy, model=model)

        # Consider the case with multiple processes
        if task.router.is_active:
            # You can use labels to distinguish between workers with different roles,
            # here we use node_id to distinguish.
            if task.router.node_id == 0:
                task.add_role(task.role.LEARNER)
            elif task.router.node_id == 1:
                task.add_role(task.role.EVALUATOR)
            else:
                task.add_role(task.role.COLLECTOR)

            # Sync their context and model between each worker.
            task.use(ContextExchanger(skip_n_iter=1))
            task.use(ModelExchanger(model))

        # Here is the part of single process pipeline.
        task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env))
        task.use(eps_greedy_handler(cfg))
        task.use(StepCollector(cfg, policy.collect_mode, collector_env))
        task.use(data_pusher(cfg, buffer_))
        task.use(OffPolicyLearner(cfg, policy.learn_mode, buffer_))
        task.use(online_logger(train_show_freq=10))
        task.use(CkptSaver(policy, cfg.exp_name, train_freq=100))

        task.run()


if __name__ == "__main__":
    main()