def forward_pass(device_type): env_name = 'atari_breakout' cfg = default_cfg(algo='appooc', env=env_name) cfg.actor_critic_share_weights = True cfg.hidden_size = 128 cfg.use_rnn = True cfg.env_framestack = 4 env = create_env(env_name, cfg=cfg) torch.set_num_threads(1) torch.backends.cudnn.benchmark = True actor_critic = create_actor_critic(cfg, env.observation_space, env.action_space) device = torch.device(device_type) actor_critic.to(device) timing = Timing() with timing.timeit('all'): batch = 128 with timing.add_time('input'): # better avoid hardcoding here... observations = dict(obs=torch.rand([batch, 4, 84, 84]).to(device)) rnn_states = torch.rand([batch, get_hidden_size(cfg)]).to(device) n = 200 for i in range(n): with timing.add_time('forward'): output = actor_critic(observations, rnn_states) log.debug('Progress %d/%d', i, n) log.debug('Timing: %s', timing)
def test_gumbel_trick(self): """ We use a Gumbel noise which seems to be faster compared to using pytorch multinomial. Here we test that those are actually equivalent. """ timing = Timing() torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True with torch.no_grad(): action_space = gym.spaces.Discrete(8) num_logits = calc_num_logits(action_space) device_type = 'cpu' device = torch.device(device_type) logits = torch.rand(self.batch_size, num_logits, device=device) * 10.0 - 5.0 if device_type == 'cuda': torch.cuda.synchronize(device) count_gumbel, count_multinomial = np.zeros( [action_space.n]), np.zeros([action_space.n]) # estimate probability mass by actually sampling both ways num_samples = 20000 action_distribution = get_action_distribution(action_space, logits) sample_actions_log_probs(action_distribution) action_distribution.sample_gumbel() with timing.add_time('gumbel'): for i in range(num_samples): action_distribution = get_action_distribution( action_space, logits) samples_gumbel = action_distribution.sample_gumbel() count_gumbel[samples_gumbel[0]] += 1 action_distribution = get_action_distribution(action_space, logits) action_distribution.sample() with timing.add_time('multinomial'): for i in range(num_samples): action_distribution = get_action_distribution( action_space, logits) samples_multinomial = action_distribution.sample() count_multinomial[samples_multinomial[0]] += 1 estimated_probs_gumbel = count_gumbel / float(num_samples) estimated_probs_multinomial = count_multinomial / float( num_samples) log.debug('Gumbel estimated probs: %r', estimated_probs_gumbel) log.debug('Multinomial estimated probs: %r', estimated_probs_multinomial) log.debug('Sampling timing: %s', timing) time.sleep(0.1) # to finish logging
def _run(self): """ Main loop of the actor worker (rollout worker). Process tasks (mainly ROLLOUT_STEP) until we get the termination signal, which usually means end of training. Currently there is no mechanism to restart dead workers if something bad happens during training. We can only retry on the initial reset(). This is definitely something to work on. """ log.info('Initializing vector env runner %d...', self.worker_idx) # workers should ignore Ctrl+C because the termination is handled in the event loop by a special msg signal.signal(signal.SIGINT, signal.SIG_IGN) torch.multiprocessing.set_sharing_strategy('file_system') timing = Timing() last_report = time.time() with torch.no_grad(): while not self.terminate: try: try: with timing.add_time('waiting'), timing.timeit('wait_actor'): tasks = self.task_queue.get_many(timeout=0.1) except Empty: tasks = [] for task in tasks: task_type, data = task if task_type == TaskType.INIT: self._init() continue if task_type == TaskType.TERMINATE: self._terminate() break # handling actual workload if task_type == TaskType.ROLLOUT_STEP: if 'work' not in timing: timing.waiting = 0 # measure waiting only after real work has started with timing.add_time('work'), timing.timeit('one_step'): self._advance_rollouts(data, timing) elif task_type == TaskType.RESET: with timing.add_time('reset'): self._handle_reset() elif task_type == TaskType.PBT: self._process_pbt_task(data) if time.time() - last_report > 5.0 and 'one_step' in timing: timing_stats = dict(wait_actor=timing.wait_actor, step_actor=timing.one_step) memory_mb = memory_consumption_mb() stats = dict(memory_actor=memory_mb) self.report_queue.put(dict(timing=timing_stats, stats=stats)) last_report = time.time() except RuntimeError as exc: log.warning('Error while processing data w: %d, exception: %s', self.worker_idx, exc) log.warning('Terminate process...') self.terminate = True self.report_queue.put(dict(critical_error=self.worker_idx)) except KeyboardInterrupt: self.terminate = True except: log.exception('Unknown exception in rollout worker') self.terminate = True if self.worker_idx <= 1: time.sleep(0.1) log.info( 'Env runner %d, CPU aff. %r, rollouts %d: timing %s', self.worker_idx, psutil.Process().cpu_affinity(), self.num_complete_rollouts, timing, )
def _run(self): # workers should ignore Ctrl+C because the termination is handled in the event loop by a special msg signal.signal(signal.SIGINT, signal.SIG_IGN) try: psutil.Process().nice(self.cfg.default_niceness) except psutil.AccessDenied: log.error('Low niceness requires sudo!') if self.cfg.device == 'gpu': cuda_envvars(self.policy_id) torch.multiprocessing.set_sharing_strategy('file_system') torch.set_num_threads(self.cfg.learner_main_loop_num_cores) timing = Timing() rollouts = [] if self.train_in_background: self.training_thread.start() else: self.initialize(timing) log.error( 'train_in_background set to False on learner %d! This is slow, use only for testing!', self.policy_id, ) while not self.terminate: while True: try: tasks = self.task_queue.get_many(timeout=0.005) for task_type, data in tasks: if task_type == TaskType.TRAIN: with timing.add_time('extract'): rollouts.extend(self._extract_rollouts(data)) # log.debug('Learner %d has %d rollouts', self.policy_id, len(rollouts)) elif task_type == TaskType.INIT: self._init() elif task_type == TaskType.TERMINATE: time.sleep(0.3) log.info('GPU learner timing: %s', timing) self._terminate() break elif task_type == TaskType.PBT: self._process_pbt_task(data) except Empty: break if self._accumulated_too_much_experience(rollouts): # if we accumulated too much experience, signal the policy workers to stop experience collection if not self.stop_experience_collection[self.policy_id]: log.debug( 'Learner %d accumulated too much experience, stop experience collection!', self.policy_id) self.stop_experience_collection[self.policy_id] = True elif self.stop_experience_collection[self.policy_id]: # otherwise, resume the experience collection if it was stopped self.stop_experience_collection[self.policy_id] = False with self.resume_experience_collection_cv: log.debug('Learner %d is resuming experience collection!', self.policy_id) self.resume_experience_collection_cv.notify_all() with torch.no_grad(): rollouts = self._process_rollouts(rollouts, timing) if not self.train_in_background: while not self.experience_buffer_queue.empty(): training_data = self.experience_buffer_queue.get() self._process_training_data(training_data, timing) self._experience_collection_rate_stats() if self.train_in_background: self.experience_buffer_queue.put(None) self.training_thread.join()
def sample(self, proc_idx): # workers should ignore Ctrl+C because the termination is handled in the event loop by a special msg signal.signal(signal.SIGINT, signal.SIG_IGN) timing = Timing() from threadpoolctl import threadpool_limits with threadpool_limits(limits=1, user_api=None): if self.cfg.set_workers_cpu_affinity: set_process_cpu_affinity(proc_idx, self.cfg.num_workers) initial_cpu_affinity = psutil.Process().cpu_affinity( ) if platform != 'darwin' else None psutil.Process().nice(10) with timing.timeit('env_init'): envs = [] env_key = ['env' for _ in range(self.cfg.num_envs_per_worker)] for env_idx in range(self.cfg.num_envs_per_worker): global_env_id = proc_idx * self.cfg.num_envs_per_worker + env_idx env_config = AttrDict(worker_index=proc_idx, vector_index=env_idx, env_id=global_env_id) env = create_env(self.cfg.env, cfg=self.cfg, env_config=env_config) log.debug( 'CPU affinity after create_env: %r', psutil.Process().cpu_affinity() if platform != 'darwin' else 'MacOS - None') env.seed(global_env_id) envs.append(env) # this is to track the performance for individual DMLab levels if hasattr(env.unwrapped, 'level_name'): env_key[env_idx] = env.unwrapped.level_name episode_length = [0 for _ in envs] episode_lengths = [deque([], maxlen=20) for _ in envs] try: with timing.timeit('first_reset'): for env_idx, env in enumerate(envs): env.reset() log.info('Process %d finished resetting %d/%d envs', proc_idx, env_idx + 1, len(envs)) self.report_queue.put( dict(proc_idx=proc_idx, finished_reset=True)) self.start_event.wait() with timing.timeit('work'): last_report = last_report_frames = total_env_frames = 0 while not self.terminate.value and total_env_frames < self.cfg.sample_env_frames_per_worker: for env_idx, env in enumerate(envs): action = env.action_space.sample() with timing.add_time(f'{env_key[env_idx]}.step'): obs, reward, done, info = env.step(action) num_frames = info.get('num_frames', 1) total_env_frames += num_frames episode_length[env_idx] += num_frames if done: with timing.add_time( f'{env_key[env_idx]}.reset'): env.reset() episode_lengths[env_idx].append( episode_length[env_idx]) episode_length[env_idx] = 0 with timing.add_time('report'): now = time.time() if now - last_report > self.report_every_sec: last_report = now frames_since_last_report = total_env_frames - last_report_frames last_report_frames = total_env_frames self.report_queue.put( dict(proc_idx=proc_idx, env_frames=frames_since_last_report)) # Extra check to make sure cpu affinity is preserved throughout the execution. # I observed weird effect when some environments tried to alter affinity of the current process, leading # to decreased performance. # This can be caused by some interactions between deep learning libs, OpenCV, MKL, OpenMP, etc. # At least user should know about it if this is happening. cpu_affinity = psutil.Process().cpu_affinity( ) if platform != 'darwin' else None assert initial_cpu_affinity == cpu_affinity, \ f'Worker CPU affinity was changed from {initial_cpu_affinity} to {cpu_affinity}!' \ f'This can significantly affect performance!' except: log.exception('Unknown exception') log.error('Unknown exception in worker %d, terminating...', proc_idx) self.report_queue.put(dict(proc_idx=proc_idx, crash=True)) time.sleep(proc_idx * 0.01 + 0.01) log.info('Process %d finished sampling. Timing: %s', proc_idx, timing) for env_idx, env in enumerate(envs): if len(episode_lengths[env_idx]) > 0: log.warning('Level %s avg episode len %d', env_key[env_idx], np.mean(episode_lengths[env_idx])) for env in envs: env.close()
def _run(self): # workers should ignore Ctrl+C because the termination is handled in the event loop by a special msg signal.signal(signal.SIGINT, signal.SIG_IGN) psutil.Process().nice(min(self.cfg.default_niceness + 2, 20)) cuda_envvars(self.policy_id) torch.multiprocessing.set_sharing_strategy('file_system') timing = Timing() with timing.timeit('init'): # initialize the Torch modules log.info('Initializing model on the policy worker %d-%d...', self.policy_id, self.worker_idx) torch.set_num_threads(1) if self.cfg.device == 'gpu': # we should already see only one CUDA device, because of env vars assert torch.cuda.device_count() == 1 self.device = torch.device('cuda', index=0) else: self.device = torch.device('cpu') self.actor_critic = create_actor_critic(self.cfg, self.obs_space, self.action_space, timing) self.actor_critic.model_to_device(self.device) for p in self.actor_critic.parameters(): p.requires_grad = False # we don't train anything here log.info('Initialized model on the policy worker %d-%d!', self.policy_id, self.worker_idx) last_report = last_cache_cleanup = time.time() last_report_samples = 0 request_count = deque(maxlen=50) # very conservative limit on the minimum number of requests to wait for # this will almost guarantee that the system will continue collecting experience # at max rate even when 2/3 of workers are stuck for some reason (e.g. doing a long env reset) # Although if your workflow involves very lengthy operations that often freeze workers, it can be beneficial # to set min_num_requests to 1 (at a cost of potential inefficiency, i.e. policy worker will use very small # batches) min_num_requests = self.cfg.num_workers // ( self.cfg.num_policies * self.cfg.policy_workers_per_policy) min_num_requests //= 3 min_num_requests = max(1, min_num_requests) # Again, very conservative timer. Only wait a little bit, then continue operation. wait_for_min_requests = 0.025 while not self.terminate: try: while self.stop_experience_collection[self.policy_id]: with self.resume_experience_collection_cv: self.resume_experience_collection_cv.wait(timeout=0.05) waiting_started = time.time() while len(self.requests) < min_num_requests and time.time( ) - waiting_started < wait_for_min_requests: try: with timing.timeit('wait_policy'), timing.add_time( 'wait_policy_total'): policy_requests = self.policy_queue.get_many( timeout=0.005) self.requests.extend(policy_requests) except Empty: pass self._update_weights(timing) with timing.timeit('one_step'), timing.add_time( 'handle_policy_step'): if self.initialized: if len(self.requests) > 0: request_count.append(len(self.requests)) self._handle_policy_steps(timing) try: task_type, data = self.task_queue.get_nowait() # task from the task_queue if task_type == TaskType.INIT: self._init() elif task_type == TaskType.TERMINATE: self.terminate = True break elif task_type == TaskType.INIT_MODEL: self._init_model(data) self.task_queue.task_done() except Empty: pass if time.time() - last_report > 3.0 and 'one_step' in timing: timing_stats = dict(wait_policy=timing.wait_policy, step_policy=timing.one_step) samples_since_last_report = self.total_num_samples - last_report_samples stats = memory_stats('policy_worker', self.device) if len(request_count) > 0: stats['avg_request_count'] = np.mean(request_count) self.report_queue.put( dict( timing=timing_stats, samples=samples_since_last_report, policy_id=self.policy_id, stats=stats, )) last_report = time.time() last_report_samples = self.total_num_samples if time.time() - last_cache_cleanup > 300.0 or ( not self.cfg.benchmark and self.total_num_samples < 1000): if self.cfg.device == 'gpu': torch.cuda.empty_cache() last_cache_cleanup = time.time() except KeyboardInterrupt: log.warning('Keyboard interrupt detected on worker %d-%d', self.policy_id, self.worker_idx) self.terminate = True except: log.exception('Unknown exception on policy worker') self.terminate = True time.sleep(0.2) log.info('Policy worker avg. requests %.2f, timing: %s', np.mean(request_count), timing)
def train(self, buffer, env_steps, agent, timing=None): if timing is None: timing = Timing() params = agent.params batch_size = params.distance_batch_size summary = None dist_step = self.step.eval(session=agent.session) prev_loss = 1e10 num_epochs = params.distance_train_epochs log.info('Train distance net %d pairs, batch %d, epochs %d', len(buffer), batch_size, num_epochs) with timing.timeit('dist_epochs'): for epoch in range(num_epochs): losses = [] with timing.add_time('shuffle'): buffer.shuffle_data() obs_first, obs_second, labels = buffer.obs_first, buffer.obs_second, buffer.labels with timing.add_time('batch'): for i in range(0, len(obs_first) - 1, batch_size): # noinspection PyProtectedMember with_summaries = agent._should_write_summaries( dist_step) and summary is None summaries = [self.summaries] if with_summaries else [] start, end = i, i + batch_size result = agent.session.run([self.loss, self.train_op] + summaries, feed_dict={ self.ph_obs_first: obs_first[start:end], self.ph_obs_second: obs_second[start:end], self.ph_labels: labels[start:end], self.ph_is_training: True, }) dist_step += 1 # noinspection PyProtectedMember agent._maybe_save(dist_step, env_steps) losses.append(result[0]) if with_summaries: summary = result[-1] agent.summary_writer.add_summary( summary, global_step=env_steps) # check loss improvement at the end of each epoch, early stop if necessary avg_loss = np.mean(losses) if avg_loss >= prev_loss: log.info( 'Early stopping after %d epochs because distance net did not improve', epoch + 1) log.info('Was %.4f now %.4f, ratio %.3f', prev_loss, avg_loss, avg_loss / prev_loss) break prev_loss = avg_loss return dist_step
def sample(self, proc_idx): # workers should ignore Ctrl+C because the termination is handled in the event loop by a special msg signal.signal(signal.SIGINT, signal.SIG_IGN) timing = Timing() psutil.Process().nice(10) num_envs = len(DMLAB30_LEVELS_THAT_USE_LEVEL_CACHE) assert self.cfg.num_workers % num_envs == 0, f'should have an integer number of workers per env, e.g. {1 * num_envs}, {2 * num_envs}, etc...' assert self.cfg.num_envs_per_worker == 1, 'use populate_cache with 1 env per worker' with timing.timeit('env_init'): env_key = 'env' env_desired_num_levels = 0 global_env_id = proc_idx * self.cfg.num_envs_per_worker env_config = AttrDict(worker_index=proc_idx, vector_index=0, env_id=global_env_id) env = create_env(self.cfg.env, cfg=self.cfg, env_config=env_config) env.seed(global_env_id) # this is to track the performance for individual DMLab levels if hasattr(env.unwrapped, 'level_name'): env_key = env.unwrapped.level_name env_level = env.unwrapped.level approx_num_episodes_per_1b_frames = DMLAB30_APPROX_NUM_EPISODES_PER_BILLION_FRAMES[ env_key] num_billions = DESIRED_TRAINING_LENGTH / int(1e9) num_workers_for_env = self.cfg.num_workers // num_envs env_desired_num_levels = int( (approx_num_episodes_per_1b_frames * num_billions) / num_workers_for_env) env_num_levels_generated = len(dmlab_level_cache.DMLAB_GLOBAL_LEVEL_CACHE[0]. all_seeds[env_level]) // num_workers_for_env log.warning('Worker %d (env %s) generated %d/%d levels!', proc_idx, env_key, env_num_levels_generated, env_desired_num_levels) time.sleep(4) env.reset() env_uses_level_cache = env.unwrapped.env_uses_level_cache self.report_queue.put(dict(proc_idx=proc_idx, finished_reset=True)) self.start_event.wait() try: with timing.timeit('work'): last_report = last_report_frames = total_env_frames = 0 while not self.terminate.value and total_env_frames < self.cfg.sample_env_frames_per_worker: action = env.action_space.sample() with timing.add_time(f'{env_key}.step'): env.step(action) total_env_frames += 1 with timing.add_time(f'{env_key}.reset'): env.reset() env_num_levels_generated += 1 log.debug('Env %s done %d/%d resets', env_key, env_num_levels_generated, env_desired_num_levels) if env_num_levels_generated >= env_desired_num_levels: log.debug('%s finished %d/%d resets, sleeping...', env_key, env_num_levels_generated, env_desired_num_levels) time.sleep(30) # free up CPU time for other envs # if env does not use level cache, there is no need to run it # let other workers proceed if not env_uses_level_cache: log.debug('Env %s does not require cache, sleeping...', env_key) time.sleep(200) with timing.add_time('report'): now = time.time() if now - last_report > self.report_every_sec: last_report = now frames_since_last_report = total_env_frames - last_report_frames last_report_frames = total_env_frames self.report_queue.put( dict(proc_idx=proc_idx, env_frames=frames_since_last_report)) if get_free_disk_space_mb(self.cfg) < 3 * 1024: log.error('Not enough disk space! %d', get_free_disk_space_mb(self.cfg)) time.sleep(200) except: log.exception('Unknown exception') log.error('Unknown exception in worker %d, terminating...', proc_idx) self.report_queue.put(dict(proc_idx=proc_idx, crash=True)) time.sleep(proc_idx * 0.1 + 0.1) log.info('Process %d finished sampling. Timing: %s', proc_idx, timing) env.close()
def localize( self, session, obs, info, maps, distance_net, frames=None, on_new_landmark=None, on_new_edge=None, timing=None, ): num_envs = len(obs) closest_landmark_idx = [-1] * num_envs # closest distance to the landmark in the existing graph (excluding new landmarks) closest_landmark_dist = [math.inf] * num_envs if all(m is None for m in maps): return closest_landmark_dist if timing is None: timing = Timing() # create a batch of all neighborhood observations from all envs for fast processing on GPU neighborhood_obs, neighborhood_hashes, current_obs, current_obs_hashes = [], [], [], [] neighborhood_infos, current_infos = [], [] total_num_neighbors = 0 neighborhood_sizes = [0] * len(maps) for env_i, m in enumerate(maps): if m is None: continue neighbor_indices = m.neighborhood() neighborhood_sizes[env_i] = len(neighbor_indices) neighborhood_obs.extend( [m.get_observation(i) for i in neighbor_indices]) neighborhood_infos.extend( [m.get_info(i) for i in neighbor_indices]) neighborhood_hashes.extend( [m.get_hash(i) for i in neighbor_indices]) current_obs.extend([obs[env_i]] * len(neighbor_indices)) current_obs_hashes.extend([hash_observation(obs[env_i])] * len(neighbor_indices)) current_infos.extend([info[env_i]] * len(neighbor_indices)) total_num_neighbors += len(neighbor_indices) assert len(neighborhood_obs) == len(current_obs) assert len(neighborhood_obs) == len(neighborhood_hashes) assert len(current_obs) == total_num_neighbors assert len(neighborhood_infos) == len(current_infos) with timing.add_time('neighbor_dist'): distances = distance_net.distances_from_obs( session, obs_first=neighborhood_obs, obs_second=current_obs, hashes_first=neighborhood_hashes, hashes_second=current_obs_hashes, infos_first=neighborhood_infos, infos_second=current_infos, ) assert len(distances) == total_num_neighbors new_landmark_candidates = [] j = 0 for env_i, m in enumerate(maps): if m is None: continue neighbor_indices = m.neighborhood() j_next = j + len(neighbor_indices) distance = distances[j:j_next] if len(neighbor_indices) != neighborhood_sizes[env_i]: log.warning( 'For env %d neighbors size expected %d, actual %d', env_i, neighborhood_sizes[env_i], len(neighbor_indices), ) assert len(neighbor_indices) == neighborhood_sizes[env_i] self._log_distances(env_i, neighbor_indices, distance) j = j_next # check if we're far enough from all landmarks in the neighborhood min_d, min_d_idx = min_with_idx(distance) closest_landmark_idx[env_i] = neighbor_indices[min_d_idx] closest_landmark_dist[env_i] = min_d if min_d >= self.new_landmark_threshold: # we're far enough from all obs in the neighborhood, might have found something new! new_landmark_candidates.append(env_i) else: # we're still sufficiently close to our neighborhood, but maybe "current landmark" has changed m.new_landmark_candidate_frames = 0 m.loop_closure_candidate_frames = 0 # crude localization if all(lm == closest_landmark_idx[env_i] for lm in m.closest_landmarks[-self.localize_frames:]): if closest_landmark_idx[env_i] != m.curr_landmark_idx: m.set_curr_landmark(closest_landmark_idx[env_i]) del neighborhood_obs del neighborhood_infos del neighborhood_hashes del current_obs del current_infos # Agents in some environments discovered landmarks that are far away from all landmarks in the immediate # vicinity. There are two possibilities: # 1) A new landmark should be created and added to the graph # 2) We're close to some other vertex in the graph - we've found a "loop closure", a new edge in a graph non_neighborhood_obs, non_neighborhood_hashes = [], [] non_neighborhoods = {} current_obs, current_obs_hashes = [], [] non_neighborhood_infos, current_infos = [], [] for env_i in new_landmark_candidates: m = maps[env_i] if m is None: continue non_neighbor_indices = m.curr_non_neighbors() non_neighborhoods[env_i] = non_neighbor_indices non_neighborhood_obs.extend( [m.get_observation(i) for i in non_neighbor_indices]) non_neighborhood_infos.extend( [m.get_info(i) for i in non_neighbor_indices]) non_neighborhood_hashes.extend( [m.get_hash(i) for i in non_neighbor_indices]) current_obs.extend([obs[env_i]] * len(non_neighbor_indices)) current_obs_hashes.extend([hash_observation(obs[env_i])] * len(non_neighbor_indices)) current_infos.extend([info[env_i]] * len(non_neighbor_indices)) assert len(non_neighborhood_obs) == len(current_obs) assert len(non_neighborhood_obs) == len(non_neighborhood_hashes) with timing.add_time('non_neigh'): # calculate distance for all non-neighbors distances = [] batch_size = 1024 for i in range(0, len(non_neighborhood_obs), batch_size): start, end = i, i + batch_size distances_batch = distance_net.distances_from_obs( session, obs_first=non_neighborhood_obs[start:end], obs_second=current_obs[start:end], hashes_first=non_neighborhood_hashes[start:end], hashes_second=current_obs_hashes[start:end], infos_first=non_neighborhood_infos[start:end], infos_second=current_infos[start:end], ) distances.extend(distances_batch) j = 0 for env_i in new_landmark_candidates: m = maps[env_i] if m is None: continue non_neighbor_indices = non_neighborhoods[env_i] j_next = j + len(non_neighbor_indices) distance = distances[j:j_next] j = j_next min_d, min_d_idx = math.inf, math.inf if len(distance) > 0: min_d, min_d_idx = min_with_idx(distance) closest_landmark_dist[env_i] = min( closest_landmark_dist[env_i], min_d) if min_d < self.loop_closure_threshold: # current observation is close to some other landmark, "close the loop" by creating a new edge m.new_landmark_candidate_frames = 0 m.loop_closure_candidate_frames += 1 closest_landmark_idx[env_i] = non_neighbor_indices[min_d_idx] # crude localization if m.loop_closure_candidate_frames >= self.localize_frames: if all(lm == closest_landmark_idx[env_i] for lm in m.closest_landmarks[-self.localize_frames:]): # we found a new edge! Cool! m.loop_closure_candidate_frames = 0 m.set_curr_landmark(closest_landmark_idx[env_i]) if on_new_edge is not None: on_new_edge(env_i) elif min_d >= self.new_landmark_threshold: m.loop_closure_candidate_frames = 0 m.new_landmark_candidate_frames += 1 # vertex is relatively far away from all vertices in the graph, we've found a new landmark! if m.new_landmark_candidate_frames >= self.localize_frames: new_landmark_idx = m.add_landmark( obs[env_i], info[env_i], update_curr_landmark=True) if frames is not None: m.graph.nodes[new_landmark_idx]['added_at'] = frames[ env_i] closest_landmark_idx[env_i] = new_landmark_idx m.new_landmark_candidate_frames = 0 if on_new_landmark is not None: on_new_landmark(env_i, new_landmark_idx) else: m.new_landmark_candidate_frames = 0 m.loop_closure_candidate_frames = 0 # update localization info for env_i in range(num_envs): m = maps[env_i] if m is None: continue assert closest_landmark_idx[env_i] >= 0 m.closest_landmarks.append(closest_landmark_idx[env_i]) # # visualize "closest" landmark # import cv2 # closest_lm = maps[0].closest_landmarks[-1] # closest_obs = maps[0].get_observation(closest_lm) # cv2.imshow('closest_obs', cv2.resize(cv2.cvtColor(closest_obs, cv2.COLOR_RGB2BGR), (420, 420))) # cv2.waitKey(1) return closest_landmark_dist
def _learn_loop(self, multi_env): """Main training loop.""" # env_steps used in tensorboard (and thus, our results) # actor_step used as global step for training step, env_steps = self.session.run( [self.actor_step, self.total_env_steps]) env_obs = multi_env.reset() obs, goals = main_observation(env_obs), goal_observation(env_obs) buffer = CuriousPPOBuffer() trajectory_buffer = TrajectoryBuffer(self.params.num_envs) self.curiosity.set_trajectory_buffer(trajectory_buffer) def end_of_training(s, es): return s >= self.params.train_for_steps or es > self.params.train_for_env_steps while not end_of_training(step, env_steps): timing = Timing() num_steps = 0 batch_start = time.time() buffer.reset() with timing.timeit('experience'): # collecting experience for rollout_step in range(self.params.rollout): actions, action_probs, values = self._policy_step( obs, goals) # wait for all the workers to complete an environment step env_obs, rewards, dones, infos = multi_env.step(actions) if self.params.graceful_episode_termination: rewards = list(rewards) for i in range(self.params.num_envs): if dones[i] and infos[i].get('prev') is not None: if infos[i]['prev'].get( 'terminated_by_timer', False): log.info('Env %d terminated by timer', i) rewards[i] += values[i] if not self.params.random_exploration: trajectory_buffer.add(obs, actions, infos, dones) next_obs, new_goals = main_observation( env_obs), goal_observation(env_obs) # calculate curiosity bonus with timing.add_time('curiosity'): if not self.params.random_exploration: bonuses = self.curiosity.generate_bonus_rewards( self.session, obs, next_obs, actions, dones, infos, ) rewards = self.params.extrinsic_reward_coeff * np.array( rewards) + bonuses # add experience from environment to the current buffer buffer.add(obs, next_obs, actions, action_probs, rewards, dones, values, goals) obs, goals = next_obs, new_goals self.process_infos(infos) num_steps += num_env_steps(infos) # last step values are required for TD-return calculation _, _, values = self._policy_step(obs, goals) buffer.values.append(values) env_steps += num_steps # calculate discounted returns and GAE buffer.finalize_batch(self.params.gamma, self.params.gae_lambda) # update actor and critic and CM with timing.timeit('train'): step = self._train_with_curiosity(step, buffer, env_steps, timing) avg_reward = multi_env.calc_avg_rewards( n=self.params.stats_episodes) avg_length = multi_env.calc_avg_episode_lengths( n=self.params.stats_episodes) self._maybe_update_avg_reward(avg_reward, multi_env.stats_num_episodes()) self._maybe_trajectory_summaries(trajectory_buffer, env_steps) self._maybe_coverage_summaries(env_steps) self.curiosity.additional_summaries( env_steps, self.summary_writer, self.params.stats_episodes, map_img=self.map_img, coord_limits=self.coord_limits, ) trajectory_buffer.reset_trajectories() fps = num_steps / (time.time() - batch_start) self._maybe_print(step, env_steps, avg_reward, avg_length, fps, timing) self._maybe_aux_summaries(env_steps, avg_reward, avg_length, fps)