import os

import numpy as np
from tensorboardX import SummaryWriter
from tqdm import tqdm


class Play(object):
    def __init__(self, env, agent, distributed=False, multi_agent=False, save_interval=100):
        self._agent = agent
        self._env = env
        self._distributed = distributed
        self._multi_agent = multi_agent
        self._brain_name = self._env.brain_names[0]
        self._save_interval = save_interval

        self._writer = SummaryWriter(os.path.split(self._agent.checkpoint_path)[0])

    def eval(self):
        if self._distributed:
            return self._run_distributed_env(False)
        return self._run_env(False)

    def learn(self, nb_episodes=1000):
        scores = list()
        for i in tqdm(range(nb_episodes)):
            if self._distributed or self._multi_agent:
                score = self._run_distributed_env(True)
            else:
                score = self._run_env(True)
            scores.append(score)
            self._ep_summary(score)
            if len(scores) > self._save_interval:
                if (np.mean(scores[-self._save_interval:])
                        > np.mean(scores[-self._save_interval - 1: -1])):
                    self._agent.save_model()

        path = os.path.split(self._agent.checkpoint_path)[0]
        np.save(os.path.join(path, "scores.npy"), np.asarray(scores))

    def _run_distributed_env(self, train):
        episode_state = list()
        episode_reward = list()
        episode_action = list()

        env_info = self._env.reset(train_mode=train)[self._brain_name]
        state = env_info.vector_observations

        episode_state.append(state)

        while True:
            action = self._agent.act(state, train)
            env_info = self._env.step(action)[self._brain_name]
            next_state = env_info.vector_observations
            reward = env_info.rewards
            done = env_info.local_done

            episode_state.append(state)
            episode_reward.append(reward)
            episode_action.append(action)

            if train and len(episode_reward) >= self._agent.n_step:
                discount = self._agent.gamma ** np.arange(self._agent.n_step)
                temp_reward = np.vstack(episode_reward[-self._agent.n_step:])

                cum_reward = [
                    np.sum(np.asarray(temp_reward[:, i]) * discount)
                    for i in range(len(env_info.agents))
                ]

                self._agent.observe(
                    episode_state[-self._agent.n_step], episode_action[-self._agent.n_step],
                    cum_reward, next_state, done)

            state = next_state
            if any(done):
                break
        if self._multi_agent:
            return np.sum(episode_reward, axis=0)
        return np.sum(np.mean(np.vstack(episode_reward), axis=1))

    def _run_env(self, train):
        episode_reward = list()
        episode_state = list()
        episode_action = list()

        env_info = self._env.reset(train_mode=train)[self._brain_name]
        state = env_info.vector_observations[0]
        episode_state.append(state)

        while True:
            action = self._agent.act(state, train)
            env_info = self._env.step(action)[self._brain_name]
            next_state = env_info.vector_observations[0]
            reward = env_info.rewards[0]
            done = env_info.local_done[0]

            episode_state.append(state)
            episode_reward.append(reward)
            episode_action.append(action)

            if train and len(episode_reward) > self._agent.n_step:
                discount = self._agent.gamma ** np.arange(self._agent.n_step)
                cum_reward = np.sum(np.asarray(episode_reward[-self._agent.n_step:]) * discount)
                self._agent.observe(
                    episode_state[-self._agent.n_step], episode_action[-self._agent.n_step],
                    cum_reward, next_state, done)

            state = next_state
            if done:
                break

        return np.sum(episode_reward)

    def _ep_summary(self, score):
        if self._multi_agent:
            for i, reward in enumerate(score):
                self._writer.add_scalar("train/reward_{}".format(i), reward, self._agent.step)
        else:
            self._writer.add_scalar("train/reward", score, self._agent.step)

        for name, param in self._agent.parameters:
            self._writer.add_histogram(
                "main/" + name, param.clone().cpu().data.numpy(), self._agent.step)

        for name, param in self._agent.target_parameters:
            self._writer.add_histogram(
                "target/" + name, param.clone().cpu().data.numpy(), self._agent.step)

f64773d21c68919193784f346e63984389e45964
<mask token>


class Play(object):

    def __init__(self, env, agent, distributed=False, multi_agent=False,
        save_interval=100):
        self._agent = agent
        self._env = env
        self._distributed = distributed
        self._multi_agent = multi_agent
        self._brain_name = self._env.brain_names[0]
        self._save_interval = save_interval
        self._writer = SummaryWriter(os.path.split(self._agent.
            checkpoint_path)[0])
    <mask token>

    def learn(self, nb_episodes=1000):
        scores = list()
        for i in tqdm(range(nb_episodes)):
            if self._distributed or self._multi_agent:
                score = self._run_distributed_env(True)
            else:
                score = self._run_env(True)
            scores.append(score)
            self._ep_summary(score)
            if len(scores) > self._save_interval:
                if np.mean(scores[-self._save_interval:]) > np.mean(scores[
                    -self._save_interval - 1:-1]):
                    self._agent.save_model()
        path = os.path.split(self._agent.checkpoint_path)[0]
        np.save(os.path.join(path, 'scores.npy'), np.asarray(scores))
    <mask token>
    <mask token>
    <mask token>

<mask token>


class Play(object):

    def __init__(self, env, agent, distributed=False, multi_agent=False,
        save_interval=100):
        self._agent = agent
        self._env = env
        self._distributed = distributed
        self._multi_agent = multi_agent
        self._brain_name = self._env.brain_names[0]
        self._save_interval = save_interval
        self._writer = SummaryWriter(os.path.split(self._agent.
            checkpoint_path)[0])

    def eval(self):
        if self._distributed:
            return self._run_distributed_env(False)
        return self._run_env(False)

    def learn(self, nb_episodes=1000):
        scores = list()
        for i in tqdm(range(nb_episodes)):
            if self._distributed or self._multi_agent:
                score = self._run_distributed_env(True)
            else:
                score = self._run_env(True)
            scores.append(score)
            self._ep_summary(score)
            if len(scores) > self._save_interval:
                if np.mean(scores[-self._save_interval:]) > np.mean(scores[
                    -self._save_interval - 1:-1]):
                    self._agent.save_model()
        path = os.path.split(self._agent.checkpoint_path)[0]
        np.save(os.path.join(path, 'scores.npy'), np.asarray(scores))

    def _run_distributed_env(self, train):
        episode_state = list()
        episode_reward = list()
        episode_action = list()
        env_info = self._env.reset(train_mode=train)[self._brain_name]
        state = env_info.vector_observations
        episode_state.append(state)
        while True:
            action = self._agent.act(state, train)
            env_info = self._env.step(action)[self._brain_name]
            next_state = env_info.vector_observations
            reward = env_info.rewards
            done = env_info.local_done
            episode_state.append(state)
            episode_reward.append(reward)
            episode_action.append(action)
            if train and len(episode_reward) >= self._agent.n_step:
                discount = self._agent.gamma ** np.arange(self._agent.n_step)
                temp_reward = np.vstack(episode_reward[-self._agent.n_step:])
                cum_reward = [np.sum(np.asarray(temp_reward[:, i]) *
                    discount) for i in range(len(env_info.agents))]
                self._agent.observe(episode_state[-self._agent.n_step],
                    episode_action[-self._agent.n_step], cum_reward,
                    next_state, done)
            state = next_state
            if any(done):
                break
        if self._multi_agent:
            return np.sum(episode_reward, axis=0)
        return np.sum(np.mean(np.vstack(episode_reward), axis=1))

    def _run_env(self, train):
        episode_reward = list()
        episode_state = list()
        episode_action = list()
        env_info = self._env.reset(train_mode=train)[self._brain_name]
        state = env_info.vector_observations[0]
        episode_state.append(state)
        while True:
            action = self._agent.act(state, train)
            env_info = self._env.step(action)[self._brain_name]
            next_state = env_info.vector_observations[0]
            reward = env_info.rewards[0]
            done = env_info.local_done[0]
            episode_state.append(state)
            episode_reward.append(reward)
            episode_action.append(action)
            if train and len(episode_reward) > self._agent.n_step:
                discount = self._agent.gamma ** np.arange(self._agent.n_step)
                cum_reward = np.sum(np.asarray(episode_reward[-self._agent.
                    n_step:]) * discount)
                self._agent.observe(episode_state[-self._agent.n_step],
                    episode_action[-self._agent.n_step], cum_reward,
                    next_state, done)
            state = next_state
            if done:
                break
        return np.sum(episode_reward)
    <mask token>

<mask token>


class Play(object):

    def __init__(self, env, agent, distributed=False, multi_agent=False,
        save_interval=100):
        self._agent = agent
        self._env = env
        self._distributed = distributed
        self._multi_agent = multi_agent
        self._brain_name = self._env.brain_names[0]
        self._save_interval = save_interval
        self._writer = SummaryWriter(os.path.split(self._agent.
            checkpoint_path)[0])

    def eval(self):
        if self._distributed:
            return self._run_distributed_env(False)
        return self._run_env(False)

    def learn(self, nb_episodes=1000):
        scores = list()
        for i in tqdm(range(nb_episodes)):
            if self._distributed or self._multi_agent:
                score = self._run_distributed_env(True)
            else:
                score = self._run_env(True)
            scores.append(score)
            self._ep_summary(score)
            if len(scores) > self._save_interval:
                if np.mean(scores[-self._save_interval:]) > np.mean(scores[
                    -self._save_interval - 1:-1]):
                    self._agent.save_model()
        path = os.path.split(self._agent.checkpoint_path)[0]
        np.save(os.path.join(path, 'scores.npy'), np.asarray(scores))

    def _run_distributed_env(self, train):
        episode_state = list()
        episode_reward = list()
        episode_action = list()
        env_info = self._env.reset(train_mode=train)[self._brain_name]
        state = env_info.vector_observations
        episode_state.append(state)
        while True:
            action = self._agent.act(state, train)
            env_info = self._env.step(action)[self._brain_name]
            next_state = env_info.vector_observations
            reward = env_info.rewards
            done = env_info.local_done
            episode_state.append(state)
            episode_reward.append(reward)
            episode_action.append(action)
            if train and len(episode_reward) >= self._agent.n_step:
                discount = self._agent.gamma ** np.arange(self._agent.n_step)
                temp_reward = np.vstack(episode_reward[-self._agent.n_step:])
                cum_reward = [np.sum(np.asarray(temp_reward[:, i]) *
                    discount) for i in range(len(env_info.agents))]
                self._agent.observe(episode_state[-self._agent.n_step],
                    episode_action[-self._agent.n_step], cum_reward,
                    next_state, done)
            state = next_state
            if any(done):
                break
        if self._multi_agent:
            return np.sum(episode_reward, axis=0)
        return np.sum(np.mean(np.vstack(episode_reward), axis=1))

    def _run_env(self, train):
        episode_reward = list()
        episode_state = list()
        episode_action = list()
        env_info = self._env.reset(train_mode=train)[self._brain_name]
        state = env_info.vector_observations[0]
        episode_state.append(state)
        while True:
            action = self._agent.act(state, train)
            env_info = self._env.step(action)[self._brain_name]
            next_state = env_info.vector_observations[0]
            reward = env_info.rewards[0]
            done = env_info.local_done[0]
            episode_state.append(state)
            episode_reward.append(reward)
            episode_action.append(action)
            if train and len(episode_reward) > self._agent.n_step:
                discount = self._agent.gamma ** np.arange(self._agent.n_step)
                cum_reward = np.sum(np.asarray(episode_reward[-self._agent.
                    n_step:]) * discount)
                self._agent.observe(episode_state[-self._agent.n_step],
                    episode_action[-self._agent.n_step], cum_reward,
                    next_state, done)
            state = next_state
            if done:
                break
        return np.sum(episode_reward)

    def _ep_summary(self, score):
        if self._multi_agent:
            for i, reward in enumerate(score):
                self._writer.add_scalar('train/reward_{}'.format(i), reward,
                    self._agent.step)
        else:
            self._writer.add_scalar('train/reward', score, self._agent.step)
        for name, param in self._agent.parameters:
            self._writer.add_histogram('main/' + name, param.clone().cpu().
                data.numpy(), self._agent.step)
        for name, param in self._agent.target_parameters:
            self._writer.add_histogram('target/' + name, param.clone().cpu(
                ).data.numpy(), self._agent.step)

import os
import numpy as np
from tensorboardX import SummaryWriter
from tqdm import tqdm


class Play(object):

    def __init__(self, env, agent, distributed=False, multi_agent=False,
        save_interval=100):
        self._agent = agent
        self._env = env
        self._distributed = distributed
        self._multi_agent = multi_agent
        self._brain_name = self._env.brain_names[0]
        self._save_interval = save_interval
        self._writer = SummaryWriter(os.path.split(self._agent.
            checkpoint_path)[0])

    def eval(self):
        if self._distributed:
            return self._run_distributed_env(False)
        return self._run_env(False)

    def learn(self, nb_episodes=1000):
        scores = list()
        for i in tqdm(range(nb_episodes)):
            if self._distributed or self._multi_agent:
                score = self._run_distributed_env(True)
            else:
                score = self._run_env(True)
            scores.append(score)
            self._ep_summary(score)
            if len(scores) > self._save_interval:
                if np.mean(scores[-self._save_interval:]) > np.mean(scores[
                    -self._save_interval - 1:-1]):
                    self._agent.save_model()
        path = os.path.split(self._agent.checkpoint_path)[0]
        np.save(os.path.join(path, 'scores.npy'), np.asarray(scores))

    def _run_distributed_env(self, train):
        episode_state = list()
        episode_reward = list()
        episode_action = list()
        env_info = self._env.reset(train_mode=train)[self._brain_name]
        state = env_info.vector_observations
        episode_state.append(state)
        while True:
            action = self._agent.act(state, train)
            env_info = self._env.step(action)[self._brain_name]
            next_state = env_info.vector_observations
            reward = env_info.rewards
            done = env_info.local_done
            episode_state.append(state)
            episode_reward.append(reward)
            episode_action.append(action)
            if train and len(episode_reward) >= self._agent.n_step:
                discount = self._agent.gamma ** np.arange(self._agent.n_step)
                temp_reward = np.vstack(episode_reward[-self._agent.n_step:])
                cum_reward = [np.sum(np.asarray(temp_reward[:, i]) *
                    discount) for i in range(len(env_info.agents))]
                self._agent.observe(episode_state[-self._agent.n_step],
                    episode_action[-self._agent.n_step], cum_reward,
                    next_state, done)
            state = next_state
            if any(done):
                break
        if self._multi_agent:
            return np.sum(episode_reward, axis=0)
        return np.sum(np.mean(np.vstack(episode_reward), axis=1))

    def _run_env(self, train):
        episode_reward = list()
        episode_state = list()
        episode_action = list()
        env_info = self._env.reset(train_mode=train)[self._brain_name]
        state = env_info.vector_observations[0]
        episode_state.append(state)
        while True:
            action = self._agent.act(state, train)
            env_info = self._env.step(action)[self._brain_name]
            next_state = env_info.vector_observations[0]
            reward = env_info.rewards[0]
            done = env_info.local_done[0]
            episode_state.append(state)
            episode_reward.append(reward)
            episode_action.append(action)
            if train and len(episode_reward) > self._agent.n_step:
                discount = self._agent.gamma ** np.arange(self._agent.n_step)
                cum_reward = np.sum(np.asarray(episode_reward[-self._agent.
                    n_step:]) * discount)
                self._agent.observe(episode_state[-self._agent.n_step],
                    episode_action[-self._agent.n_step], cum_reward,
                    next_state, done)
            state = next_state
            if done:
                break
        return np.sum(episode_reward)

    def _ep_summary(self, score):
        if self._multi_agent:
            for i, reward in enumerate(score):
                self._writer.add_scalar('train/reward_{}'.format(i), reward,
                    self._agent.step)
        else:
            self._writer.add_scalar('train/reward', score, self._agent.step)
        for name, param in self._agent.parameters:
            self._writer.add_histogram('main/' + name, param.clone().cpu().
                data.numpy(), self._agent.step)
        for name, param in self._agent.target_parameters:
            self._writer.add_histogram('target/' + name, param.clone().cpu(
                ).data.numpy(), self._agent.step)

import os

import numpy as np
from tensorboardX import SummaryWriter
from tqdm import tqdm


class Play(object):
    def __init__(self, env, agent, distributed=False, multi_agent=False, save_interval=100):
        self._agent = agent
        self._env = env
        self._distributed = distributed
        self._multi_agent = multi_agent
        self._brain_name = self._env.brain_names[0]
        self._save_interval = save_interval

        self._writer = SummaryWriter(os.path.split(self._agent.checkpoint_path)[0])

    def eval(self):
        if self._distributed:
            return self._run_distributed_env(False)
        return self._run_env(False)

    def learn(self, nb_episodes=1000):
        scores = list()
        for i in tqdm(range(nb_episodes)):
            if self._distributed or self._multi_agent:
                score = self._run_distributed_env(True)
            else:
                score = self._run_env(True)
            scores.append(score)
            self._ep_summary(score)
            if len(scores) > self._save_interval:
                if (np.mean(scores[-self._save_interval:])
                        > np.mean(scores[-self._save_interval - 1: -1])):
                    self._agent.save_model()

        path = os.path.split(self._agent.checkpoint_path)[0]
        np.save(os.path.join(path, "scores.npy"), np.asarray(scores))

    def _run_distributed_env(self, train):
        episode_state = list()
        episode_reward = list()
        episode_action = list()

        env_info = self._env.reset(train_mode=train)[self._brain_name]
        state = env_info.vector_observations

        episode_state.append(state)

        while True:
            action = self._agent.act(state, train)
            env_info = self._env.step(action)[self._brain_name]
            next_state = env_info.vector_observations
            reward = env_info.rewards
            done = env_info.local_done

            episode_state.append(state)
            episode_reward.append(reward)
            episode_action.append(action)

            if train and len(episode_reward) >= self._agent.n_step:
                discount = self._agent.gamma ** np.arange(self._agent.n_step)
                temp_reward = np.vstack(episode_reward[-self._agent.n_step:])

                cum_reward = [
                    np.sum(np.asarray(temp_reward[:, i]) * discount)
                    for i in range(len(env_info.agents))
                ]

                self._agent.observe(
                    episode_state[-self._agent.n_step], episode_action[-self._agent.n_step],
                    cum_reward, next_state, done)

            state = next_state
            if any(done):
                break
        if self._multi_agent:
            return np.sum(episode_reward, axis=0)
        return np.sum(np.mean(np.vstack(episode_reward), axis=1))

    def _run_env(self, train):
        episode_reward = list()
        episode_state = list()
        episode_action = list()

        env_info = self._env.reset(train_mode=train)[self._brain_name]
        state = env_info.vector_observations[0]
        episode_state.append(state)

        while True:
            action = self._agent.act(state, train)
            env_info = self._env.step(action)[self._brain_name]
            next_state = env_info.vector_observations[0]
            reward = env_info.rewards[0]
            done = env_info.local_done[0]

            episode_state.append(state)
            episode_reward.append(reward)
            episode_action.append(action)

            if train and len(episode_reward) > self._agent.n_step:
                discount = self._agent.gamma ** np.arange(self._agent.n_step)
                cum_reward = np.sum(np.asarray(episode_reward[-self._agent.n_step:]) * discount)
                self._agent.observe(
                    episode_state[-self._agent.n_step], episode_action[-self._agent.n_step],
                    cum_reward, next_state, done)

            state = next_state
            if done:
                break

        return np.sum(episode_reward)

    def _ep_summary(self, score):
        if self._multi_agent:
            for i, reward in enumerate(score):
                self._writer.add_scalar("train/reward_{}".format(i), reward, self._agent.step)
        else:
            self._writer.add_scalar("train/reward", score, self._agent.step)

        for name, param in self._agent.parameters:
            self._writer.add_histogram(
                "main/" + name, param.clone().cpu().data.numpy(), self._agent.step)

        for name, param in self._agent.target_parameters:
            self._writer.add_histogram(
                "target/" + name, param.clone().cpu().data.numpy(), self._agent.step)
