强化学习——Advantage Actor-Critic(A2C)¶

#This code is from openai baseline
#https://github.com/openai/baselines/tree/master/baselines/common/vec_env

import numpy as np
from multiprocessing import Process, Pipe

def worker(remote, parent_remote, env_fn_wrapper):
    parent_remote.close()
    env = env_fn_wrapper.x()
    while True:
        cmd, data = remote.recv()
        if cmd == 'step':
            ob, reward, done, info = env.step(data)
            # ob, reward, done, info = env.step(1)

            if done:
                ob = env.reset()
            remote.send((ob, reward, done, info))
        elif cmd == 'reset':
            ob = env.reset()
            remote.send(ob)
        elif cmd == 'reset_task':
            ob = env.reset_task()
            remote.send(ob)
        elif cmd == 'close':
            remote.close()
            break
        elif cmd == 'get_spaces':
            remote.send((env.observation_space, env.action_space))
        else:
            raise NotImplementedError

class VecEnv:
    """
    An abstract asynchronous, vectorized environment.
    """
    def __init__(self, num_envs, observation_space, action_space):
        self.num_envs = num_envs
        self.observation_space = observation_space
        self.action_space = action_space

    def reset(self):
        """
        Reset all the environments and return an array of
        observations, or a tuple of observation arrays.
        If step_async is still doing work, that work will
        be cancelled and step_wait() should not be called
        until step_async() is invoked again.
        """
        pass

    def step_async(self, actions):
        """
        Tell all the environments to start taking a step
        with the given actions.
        Call step_wait() to get the results of the step.
        You should not call this if a step_async run is
        already pending.
        """
        pass

    def step_wait(self):
        """
        Wait for the step taken with step_async().
        Returns (obs, rews, dones, infos):
         - obs: an array of observations, or a tuple of
                arrays of observations.
         - rews: an array of rewards
         - dones: an array of "episode done" booleans
         - infos: a sequence of info objects
        """
        pass

    def close(self):
        """
        Clean up the environments' resources.
        """
        pass

    def step(self, actions):
        self.step_async(actions)
        return self.step_wait()

    
class CloudpickleWrapper:
    """
    Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
    """
    def __init__(self, x):
        self.x = x
    def __getstate__(self):
        import cloudpickle
        return cloudpickle.dumps(self.x)
    def __setstate__(self, ob):
        import pickle
        self.x = pickle.loads(ob)

        
class SubprocVecEnv(VecEnv):
    def __init__(self, env_fns, spaces=None):
        """
        envs: list of gym environments to run in subprocesses
        """
        self.waiting = False
        self.closed = False
        nenvs = len(env_fns)
        self.nenvs = nenvs
        self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
        self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
            for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
        for p in self.ps:
            p.daemon = True # if the main process crashes, we should not cause things to hang
            p.start()
        for remote in self.work_remotes:
            remote.close()

        self.remotes[0].send(('get_spaces', None))
        observation_space, action_space = self.remotes[0].recv()
        VecEnv.__init__(self, len(env_fns), observation_space, action_space)

    def step_async(self, actions):
        for remote, action in zip(self.remotes, actions):
            remote.send(('step', action))
        self.waiting = True

    def step_wait(self):
        results = [remote.recv() for remote in self.remotes]
        self.waiting = False
        obs, rews, dones, infos = zip(*results)
        return np.stack(obs), np.stack(rews), np.stack(dones), infos

    def reset(self):
        for remote in self.remotes:
            remote.send(('reset', None))
        return np.stack([remote.recv() for remote in self.remotes])

    def reset_task(self):
        for remote in self.remotes:
            remote.send(('reset_task', None))
        return np.stack([remote.recv() for remote in self.remotes])

    def close(self):
        if self.closed:
            return
        if self.waiting:
            for remote in self.remotes:            
                remote.recv()
        for remote in self.remotes:
            remote.send(('close', None))
        for p in self.ps:
            p.join()
            self.closed = True
            
    def __len__(self):
        return self.nenvs

writer = LogWriter(logdir="./log") 

#from multiprocessing_env import SubprocVecEnv

num_envs = 8
env_name = "CartPole-v0"

def make_env():
    def _thunk():
        env = gym.make(env_name)
        return env
    return _thunk

plt.ion()
envs = [make_env() for i in range(num_envs)]
envs = SubprocVecEnv(envs) # 8 env

env = gym.make(env_name) # a single env

class ActorCritic(nn.Layer):
    def __init__(self, num_inputs, num_outputs, hidden_size, std=0.0):
        super().__init__()
        nn.initializer.set_global_initializer(nn.initializer.XavierNormal(), nn.initializer.Constant(value=0.))
        
        self.critic = nn.Sequential(
            nn.Linear(num_inputs, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, 1)
        )
        
        self.actor = nn.Sequential(
            nn.Linear(num_inputs, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, num_outputs),
            nn.Softmax(axis=1),
        )
    
    def forward(self, x):
        value = self.critic(x)
        probs = self.actor(x)
        dist  = Categorical(probs)
        return dist, value


def test_env(vis=False):
    state = env.reset()
    if vis: env.render()
    done = False
    total_reward = 0
    while not done:
        state = paddle.to_tensor(state,dtype="float32").unsqueeze(0)
        dist, _ = model(state)
        next_state, reward, done, _ = env.step(dist.sample([1]).cpu().numpy()[0][0])        
        state = next_state
        if vis: env.render()
        total_reward += reward
    return total_reward


def compute_returns(next_value, rewards, masks, gamma=0.99):
    R = next_value
    returns = []
    for step in reversed(range(len(rewards))):
        R = rewards[step] + gamma * R * masks[step]
        returns.insert(0, R)
    return returns

def plot(frame_idx, rewards):
    plt.plot(rewards,'b-')
    plt.title('frame %s. reward: %s' % (frame_idx, rewards[-1]))
    plt.pause(0.0001)

num_inputs  = envs.observation_space.shape[0]
num_outputs = envs.action_space.n

# Hyper params:
hidden_size = 256
lr          = 1e-3
num_steps   = 8

model = ActorCritic(num_inputs, num_outputs, hidden_size)
optimizer = optim.Adam(parameters=model.parameters(),learning_rate=lr)
save_model_path = "models/A2C_model.pdparams"
if os.path.exists(save_model_path):
    model_state_dict  = paddle.load(save_model_path)
    model.set_state_dict(model_state_dict )
    print(' Model loaded')