tensorforce/execution/runner.py
def run(self, num_timesteps=None, num_episodes=None, max_episode_timesteps=None, deterministic=False,
            episode_finished=None, summary_report=None, summary_interval=None, timesteps=None, episodes=None, testing=False, sleep=None
            ):
        """
        Args:
            timesteps (int): Deprecated; see num_timesteps.
            episodes (int): Deprecated; see num_episodes.
        """

        # deprecation warnings
        if timesteps is not None:
            num_timesteps = timesteps
            warnings.warn("WARNING: `timesteps` parameter is deprecated, use `num_timesteps` instead.",
                          category=DeprecationWarning)
        if episodes is not None:
            num_episodes = episodes
            warnings.warn("WARNING: `episodes` parameter is deprecated, use `num_episodes` instead.",
                          category=DeprecationWarning)

        # figure out whether we are using the deprecated way of "episode_finished" reporting
        old_episode_finished = False
        if episode_finished is not None and len(getargspec(episode_finished).args) == 1:
            old_episode_finished = True

        # Keep track of episode reward and episode length for statistics.
        self.start_time = time.time()

        self.agent.reset()

        if num_episodes is not None:
            num_episodes += self.agent.episode

        if num_timesteps is not None:
            num_timesteps += self.agent.timestep

        # add progress bar
        with tqdm(total=num_episodes) as pbar:
            # episode loop
            while True:
                episode_start_time = time.time()
                state = self.environment.reset()
                self.agent.reset()

                # Update global counters.
                self.global_episode = self.agent.episode  # global value (across all agents)
                self.global_timestep = self.agent.timestep  # global value (across all agents)

                episode_reward = 0
                self.current_timestep = 0

                # time step (within episode) loop
                while True:
                    action = self.agent.act(states=state, deterministic=deterministic)

                    reward = 0
                    for _ in xrange(self.repeat_actions):
                        state, terminal, step_reward = self.environment.execute(action=action)
                        reward += step_reward
                        if terminal:
                            break

                    if max_episode_timesteps is not None and self.current_timestep >= max_episode_timesteps:
                        terminal = True

                    if not testing:
                        self.agent.observe(terminal=terminal, reward=reward)

                    self.global_timestep += 1
                    self.current_timestep += 1
                    episode_reward += reward

                    if terminal or self.agent.should_stop():  # TODO: should_stop also terminate?
                        break

                    if sleep is not None:
                        time.sleep(sleep)

                # Update our episode stats.
                time_passed = time.time() - episode_start_time
                self.episode_rewards.append(episode_reward)
                self.episode_timesteps.append(self.current_timestep)
                self.episode_times.append(time_passed)

                self.global_episode += 1
                pbar.update(1)

                # Check, whether we should stop this run.
                if episode_finished is not None:
                    # deprecated way (passing in only runner object):
                    if old_episode_finished:
                        if not episode_finished(self):
                            break
                    # new unified way (passing in BaseRunner AND some worker ID):
                    elif not episode_finished(self, self.id):
                        break
                if (num_episodes is not None and self.global_episode >= num_episodes) or \
                        (num_timesteps is not None and self.global_timestep >= num_timesteps) or \
                        self.agent.should_stop():
                    break
            pbar.update(num_episodes - self.global_episode)
def run(self, num_timesteps=None, num_episodes=None, max_episode_timesteps=None, deterministic=False,
            episode_finished=None, summary_report=None, summary_interval=None, timesteps=None, episodes=None, testing=False, sleep=None
            ):
        """
        Args:
            timesteps (int): Deprecated; see num_timesteps.
            episodes (int): Deprecated; see num_episodes.
        """

        # deprecation warnings
        if timesteps is not None:
            num_timesteps = timesteps
            warnings.warn("WARNING: `timesteps` parameter is deprecated, use `num_timesteps` instead.",
                          category=DeprecationWarning)
        if episodes is not None:
            num_episodes = episodes
            warnings.warn("WARNING: `episodes` parameter is deprecated, use `num_episodes` instead.",
                          category=DeprecationWarning)

        # figure out whether we are using the deprecated way of "episode_finished" reporting
        old_episode_finished = False
        if episode_finished is not None and len(getargspec(episode_finished).args) == 1:
            old_episode_finished = True

        # Keep track of episode reward and episode length for statistics.
        self.start_time = time.time()

        self.agent.reset()

        if num_episodes is not None:
            num_episodes += self.agent.episode

        if num_timesteps is not None:
            num_timesteps += self.agent.timestep

        # add progress bar
        with tqdm(total=num_episodes) as pbar:
            # episode loop
            while True:
                episode_start_time = time.time()
                state = self.environment.reset()
                self.agent.reset()

                # Update global counters.
                self.global_episode = self.agent.episode  # global value (across all agents)
                self.global_timestep = self.agent.timestep  # global value (across all agents)

                episode_reward = 0
                self.current_timestep = 0

                # time step (within episode) loop
                while True:
                    action = self.agent.act(states=state, deterministic=deterministic)

                    reward = 0
                    for _ in xrange(self.repeat_actions):
                        state, terminal, step_reward = self.environment.execute(action=action)
                        reward += step_reward
                        if terminal:
                            break

                    if max_episode_timesteps is not None and self.current_timestep >= max_episode_timesteps:
                        terminal = True

                    if not testing:
                        self.agent.observe(terminal=terminal, reward=reward)

                    self.global_timestep += 1
                    self.current_timestep += 1
                    episode_reward += reward

                    if terminal or self.agent.should_stop():  # TODO: should_stop also terminate?
                        break

                    if sleep is not None:
                        time.sleep(sleep)

                # Update our episode stats.
                time_passed = time.time() - episode_start_time
                self.episode_rewards.append(episode_reward)
                self.episode_timesteps.append(self.current_timestep)
                self.episode_times.append(time_passed)

                self.global_episode += 1
                pbar.update(1)

                # Check, whether we should stop this run.
                if episode_finished is not None:
                    # deprecated way (passing in only runner object):
                    if old_episode_finished:
                        if not episode_finished(self):
                            break
                    # new unified way (passing in BaseRunner AND some worker ID):
                    elif not episode_finished(self, self.id):
                        break
                if (num_episodes is not None and self.global_episode >= num_episodes) or \
                        (num_timesteps is not None and self.global_timestep >= num_timesteps) or \
                        self.agent.should_stop():
                    break
            pbar.update(num_episodes - self.global_episode)
Args:
            timesteps (int): Deprecated; see num_timesteps.
            episodes (int): Deprecated; see num_episodes.