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)
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 ) : # 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.
Args : timesteps ( int ) : Deprecated ; see num_timesteps . episodes ( int ) : Deprecated ; see num_episodes .