def test_render_videos(): env_id = "Hopper-v1" env = make_with_torque_removed(env_id) segments = segments_from_rand_rollout(env_id, make_with_torque_removed, n_desired_segments=1, clip_length_in_seconds=CLIP_LENGTH) for idx, segment in enumerate(segments): local_path = osp.join(TEST_RENDER_DIR, 'test-%s.mp4' % idx) print("Writing segment to: %s" % local_path) write_segment_to_video(segment, fname=local_path, env=env)
def test_render_videos(): env = make_with_torque_removed("Hopper-v1") collector = RandomRolloutSegmentCollector(20000, env=env) rl_teacher.agent.trpo.run_trpo_mujoco.train( num_timesteps=8000, env=env, seed=0, predictor=collector, random_rollout=True, ) segments = collector.segments tmp_media_dir = '/tmp/rl_teacher_media_test' for segment in segments: local_path = osp.join(tmp_media_dir, str(uuid.uuid4()) + '.mp4') print("Writing segment to: %s" % local_path) write_segment_to_video(segment, fname=local_path, env=env)
def _write_and_upload_video(env_id, gcs_path, local_path, segment): env = make_with_torque_removed(env_id) write_segment_to_video(segment, fname=local_path, env=env) upload_to_gcs(local_path, gcs_path)
def make_env(): return make_with_torque_removed(env_id)
def main(): import argparse parser = argparse.ArgumentParser() parser.add_argument('-e', '--env_id', required=True) parser.add_argument('-p', '--predictor', required=True) parser.add_argument('-n', '--name', required=True) parser.add_argument('-s', '--seed', default=1, type=int) parser.add_argument('-w', '--workers', default=4, type=int) parser.add_argument('-l', '--n_labels', default=None, type=int) parser.add_argument('-L', '--pretrain_labels', default=None, type=int) parser.add_argument('-t', '--num_timesteps', default=5e6, type=int) parser.add_argument('-a', '--agent', default="parallel_trpo", type=str) parser.add_argument('-i', '--pretrain_iters', default=10000, type=int) parser.add_argument('-V', '--no_videos', action="store_true") parser.add_argument('-x', '--human_labels', default=1000, type=int) args = parser.parse_args() print("Setting things up...") env_id = args.env_id run_name = "%s/%s-%s" % (env_id, args.name, int(time())) summary_writer = make_summary_writer(run_name) env = make_with_torque_removed(env_id) num_timesteps = int(args.num_timesteps) experiment_name = slugify(args.name) if args.predictor == "rl": predictor = TraditionalRLRewardPredictor(summary_writer) else: agent_logger = AgentLogger(summary_writer) pretrain_labels = args.pretrain_labels if args.pretrain_labels else args.n_labels // 4 if args.n_labels: label_schedule = LabelAnnealer(agent_logger, final_timesteps=num_timesteps, final_labels=args.n_labels, pretrain_labels=pretrain_labels) else: print( "No label limit given. We will request one label every few seconds." ) label_schedule = ConstantLabelSchedule( pretrain_labels=pretrain_labels) if args.predictor == "synth": comparison_collector = SyntheticComparisonCollector( run_name, args.human_labels) elif args.predictor == "human": bucket = os.environ.get('RL_TEACHER_GCS_BUCKET') assert bucket and bucket.startswith( "gs://" ), "env variable RL_TEACHER_GCS_BUCKET must start with gs://" comparison_collector = HumanComparisonCollector( env_id, experiment_name=experiment_name) else: raise ValueError("Bad value for --predictor: %s" % args.predictor) predictor = ComparisonRewardPredictor( env, summary_writer, comparison_collector=comparison_collector, agent_logger=agent_logger, label_schedule=label_schedule, ) print( "Starting random rollouts to generate pretraining segments. No learning will take place..." ) pretrain_segments = segments_from_rand_rollout( env_id, make_with_torque_removed, n_desired_segments=pretrain_labels * 2, clip_length_in_seconds=CLIP_LENGTH, workers=args.workers) for i in range( pretrain_labels): # Turn our random segments into comparisons comparison_collector.add_segment_pair( pretrain_segments[i], pretrain_segments[i + pretrain_labels]) # Sleep until the human has labeled most of the pretraining comparisons while len(comparison_collector.labeled_comparisons) < int( pretrain_labels * 0.75): comparison_collector.label_unlabeled_comparisons() if args.predictor == "synth": print("%s synthetic labels generated... " % (len(comparison_collector.labeled_comparisons))) elif args.predictor == "human": print( "%s/%s comparisons labeled. Please add labels w/ the human-feedback-api. Sleeping... " % (len(comparison_collector.labeled_comparisons), pretrain_labels)) sleep(5) # Start the actual training for i in range(args.pretrain_iters): predictor.train_predictor() # Train on pretraining labels if i % 100 == 0: print("%s/%s predictor pretraining iters... " % (i, args.pretrain_iters)) # Wrap the predictor to capture videos every so often: if not args.no_videos: predictor = SegmentVideoRecorder(predictor, env, save_dir=osp.join( '/tmp/rl_teacher_vids', run_name)) # We use a vanilla agent from openai/baselines that contains a single change that blinds it to the true reward # The single changed section is in `rl_teacher/agent/trpo/core.py` print("Starting joint training of predictor and agent") if args.agent == "parallel_trpo": train_parallel_trpo( env_id=env_id, make_env=make_with_torque_removed, predictor=predictor, summary_writer=summary_writer, workers=args.workers, runtime=(num_timesteps / 1000), max_timesteps_per_episode=get_timesteps_per_episode(env), timesteps_per_batch=8000, max_kl=0.001, seed=args.seed, ) elif args.agent == "pposgd_mpi": def make_env(): return make_with_torque_removed(env_id) train_pposgd_mpi(make_env, num_timesteps=num_timesteps, seed=args.seed, predictor=predictor) else: raise ValueError("%s is not a valid choice for args.agent" % args.agent)
def env_fn(): from rl_teacher.envs import make_with_torque_removed env = make_with_torque_removed(env_id) env.seed(seed) return env
def main(): import argparse parser = argparse.ArgumentParser() parser.add_argument('-e', '--env_id', default="ShortHopper-v1", type=str) parser.add_argument('-p', '--predictor', default="human", type=str) parser.add_argument('-n', '--name', default="human-175-hopper", type=str) parser.add_argument('-s', '--seed', default=6, type=int) parser.add_argument('-w', '--workers', default=4, type=int) parser.add_argument('-l', '--n_labels', default=None, type=int) parser.add_argument('-L', '--pretrain_labels', default=20, type=int) parser.add_argument('-t', '--num_timesteps', default=5e6, type=int) parser.add_argument('-a', '--agent', default="pposgd_mpi", type=str) parser.add_argument('-i', '--pretrain_iters', default=1, type=int) parser.add_argument('-V', '--no_videos', action="store_true") parser.add_argument('--log_path', help='Directory to save learning curve data.', default='tmp/openaiTest', type=str) args = parser.parse_args() print("Setting things up...") env_id = args.env_id run_name = "%s/%s-%s" % (env_id, args.name, int(time())) summary_writer = make_summary_writer(run_name) env = make_with_torque_removed(env_id) num_timesteps = int(args.num_timesteps) experiment_name = slugify(args.name) if args.predictor == "rl": predictor = TraditionalRLRewardPredictor(summary_writer) else: agent_logger = AgentLogger(summary_writer) pretrain_labels = args.pretrain_labels if args.pretrain_labels else args.n_labels // 4 #online and offline if args.n_labels: label_schedule = LabelAnnealer(agent_logger, final_timesteps=num_timesteps, final_labels=args.n_labels, pretrain_labels=pretrain_labels) else: print( "No label limit given. We will request one label every few seconds." ) label_schedule = ConstantLabelSchedule( pretrain_labels=pretrain_labels) if args.predictor == "synth": comparison_collector = SyntheticComparisonCollector() elif args.predictor == "human": bucket = os.environ.get('RL_TEACHER_GCS_BUCKET') bucket = "gs://rl-teacher-preference" #assert bucket and bucket.startswith("gs://"), "env variable RL_TEACHER_GCS_BUCKET must start with gs://" comparison_collector = HumanComparisonCollector( env_id, experiment_name=experiment_name) else: raise ValueError("Bad value for --predictor: %s" % args.predictor) predictor = ComparisonRewardPredictor( env, summary_writer, comparison_collector=comparison_collector, agent_logger=agent_logger, label_schedule=label_schedule, ) # print("Starting random rollouts to generate pretraining segments. No learning will take place...") # pretrain_segments = segments_from_rand_rollout( # env_id, make_with_torque_removed, n_desired_segments=pretrain_labels * 2, # clip_length_in_seconds=CLIP_LENGTH, workers=args.workers) # for i in range(pretrain_labels): # Turn our random segments into comparisons # comparison_collector.add_segment_pair(pretrain_segments[i], pretrain_segments[i + pretrain_labels]) # # # Sleep until the human has labeled most of the pretraining comparisons # while len(comparison_collector.labeled_comparisons) < int(pretrain_labels * 0.75): # comparison_collector.label_unlabeled_comparisons() # if args.predictor == "synth": # print("%s synthetic labels generated... " % (len(comparison_collector.labeled_comparisons))) # elif args.predictor == "human": # print("%s/%s comparisons labeled. Please add labels w/ the human-feedback-api. Sleeping... " % ( # len(comparison_collector.labeled_comparisons), pretrain_labels)) # sleep(5) # # # Start the actual training # # for i in range(args.pretrain_iters): # predictor.train_predictor() # Train on pretraining labels # if i % 10 == 0: # print("%s/%s predictor pretraining iters... " % (i, args.pretrain_iters)) #saver = tf.train.Saver(max_to_keep=5) #save_path = saver.save(sess, "/tmp/GAN/GAN_preference_based_model.ckpt") #print("Model saved in path: %s" % save_path) # Wrap the predictor to capture videos every so often: if not args.no_videos: predictor = SegmentVideoRecorder(predictor, env, save_dir=osp.join( '/tmp/rl_teacher_vids', run_name)) # We use a vanilla agent from openai/baselines that contains a single change that blinds it to the true reward # The single changed section is in `rl_teacher/agent/trpo/core.py` print("Starting joint training of predictor and agent") if args.agent == "parallel_trpo": train_parallel_trpo( env_id=env_id, make_env=make_with_torque_removed, predictor=predictor, summary_writer=summary_writer, workers=args.workers, runtime=(num_timesteps / 1000), max_timesteps_per_episode=get_timesteps_per_episode(env), timesteps_per_batch=8000, max_kl=0.001, seed=args.seed, ) elif args.agent == "pposgd_mpi": def make_env(): return make_with_torque_removed(env_id) try: from mpi4py import MPI except ImportError: MPI = None def configure_logger(log_path, **kwargs): if log_path is not None: logger.configure(log_path) else: logger.configure(**kwargs) if MPI is None or MPI.COMM_WORLD.Get_rank() == 0: rank = 0 configure_logger(args.log_path) else: rank = MPI.COMM_WORLD.Get_rank() configure_logger(args.log_path, format_strs=[]) train_pposgd_mpi(make_env, num_timesteps=num_timesteps, seed=args.seed, predictor=predictor) else: raise ValueError("%s is not a valid choice for args.agent" % args.agent)
tb_logger.log(k, v) tb_logger.summary_step += 1 stats["Time elapsed"] = "%.2f mins" % ( (time.time() - start_time) / 60.0) print("\n********** Iteration {} ************".format(i)) for k, v in stats.items(): print(k + ": " + " " * (40 - len(k)) + str(v)) if entropy != entropy: exit(-1) logging.getLogger().setLevel(logging.DEBUG) # env = envs.make(args.task) env_id = args.task env = make_with_torque_removed(env_id) # env = Monitor(env, '/tmp/trpo_ilyasu') # def capped_cubic_video_schedule(episode_id): # if episode_id < 1000: # return int(round(episode_id ** (1. / 3))) ** 3 == episode_id # else: # return episode_id % 1000 == 0 agent = TRPO(env) agent.learn() from sys import argv print('python {}'.format(' '.join(argv)))
def _write_and_upload_video(env_id, gcs_path, local_path, segment): os.mkdir('/tmp/233') env = make_with_torque_removed(env_id) write_segment_to_video(segment, fname=local_path, env=env)