def main(): """Run DQN until the environment throws an exception.""" base_path = "results/rainbow/6/" env = make_env(stack=False, scale_rew=False, render=None, monitor=base_path + "train_monitor", episodic_life=True) # I think the env itself allows Backtracking env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) config = tf.ConfigProto() config.gpu_options.allow_growth = True config.gpu_options.per_process_gpu_memory_fraction = 0.8 with tf.Session(config=config) as sess: dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)) player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) optimize = dqn.optimize(learning_rate=1e-4) saver = tf.train.Saver(name="rainbow") sess.run(tf.global_variables_initializer()) saver.save(sess, base_path + "training", global_step=0) try: dqn.train(num_steps=2_000_000, # Make sure an exception arrives before we stop. player=player, replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, train_interval=1, target_interval=8192, batch_size=64, min_buffer_size=20000, handle_ep=handle_ep) # in seconds except KeyboardInterrupt: print("keyboard interrupt") print("finishing") saver.save(sess, base_path + "final", global_step=2_000_000)
def main(): env = AllowBacktracking(make_env(stack=False, scale_rew=False)) env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-421, max_val=421)) player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) optimize = dqn.optimize(learning_rate=1e-4) sess.run(tf.global_variables_initializer()) dqn.train(num_steps=2000000, player=player, replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, train_interval=1, target_interval=64, batch_size=32, min_buffer_size=25000)
def main(): """Run DQN until the environment throws an exception.""" env = make(game='SonicTheHedgehog-Genesis', state='GreenHillZone.Act1') env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 with tf.Session(config=config) as sess: dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)) player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) optimize = dqn.optimize(learning_rate=1e-4) sess.run(tf.global_variables_initializer()) dqn.train( num_steps=2000000, # Make sure an exception arrives before we stop. player=player, replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, train_interval=1, target_interval=8192, batch_size=32, min_buffer_size=20000)
def main(): """Run DQN until the environment throws an exception.""" env = AllowBacktracking(make_env(stack=False, scale_rew=False)) env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 with tf.Session(config=config) as sess: dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)) player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) optimize = dqn.optimize(learning_rate=1e-4) sess.run(tf.global_variables_initializer()) dqn.train( num_steps=2000000, # Make sure an exception arrives before we stop. player=player, replay_buffer=StochasticMaxStochasticDeltaDeletionPRB(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, train_interval=1, target_interval=8192, batch_size=32, min_buffer_size=20000)
def main(): """Run DQN until the environment throws an exception.""" env = make(game='SonicTheHedgehog-Genesis', state='GreenHillZone.Act1') env = AllowBacktracking(make_local_env(env, stack=False, scale_rew=False)) env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 with tf.Session(config=config) as sess: dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)) player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) optimize = dqn.optimize(learning_rate=1e-4) sess.run(tf.global_variables_initializer()) dqn.train(num_steps=num_steps, # Make sure an exception arrives before we stop. player=player, replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, train_interval=1, target_interval=8192, batch_size=32, min_buffer_size=20000) print(tf.trainable_variables()) save_path='/home/noob/retro-noob/rainbow/params/params' utils.save_state(save_path+'_tf_saver') with tf.variable_scope('model'): params = tf.trainable_variables() ps = sess.run(params) joblib.dump(ps, save_path + '_joblib')
def main(): """Run DQN until the environment throws an exception.""" env = AllowBacktracking(make_env(stack=False, scale_rew=False)) env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 with tf.Session(config=config) as sess: dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)) player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) """ Create a TF Op that optimizes the objective. Args: learning_rate: the Adam learning rate. epsilon: the Adam epsilon. """ optimize = dqn.optimize(learning_rate=6.25e-5, epsilon=1.5e-4) sess.run(tf.global_variables_initializer()) """ Run an automated training loop. This is meant to provide a convenient way to run a standard training loop without any modifications. You may get more flexibility by writing your own training loop. Args: num_steps: the number of timesteps to run. player: the Player for gathering experience. replay_buffer: the ReplayBuffer for experience. optimize_op: a TF Op to optimize the model. train_interval: timesteps per training step. target_interval: number of timesteps between target network updates. batch_size: the size of experience mini-batches. min_buffer_size: minimum replay buffer size before training is performed. tf_schedules: a sequence of TFSchedules that are updated with the number of steps taken. handle_ep: called with information about every completed episode. timeout: if set, this is a number of seconds after which the training loop should exit. """ dqn.train( num_steps=1000000, # Make sure an exception arrives before we stop. player=player, replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, train_interval=1, target_interval=8192, batch_size=32, min_buffer_size=20000)
def main(): """Run DQN until the environment throws an exception.""" env = AllowBacktracking(make_env(stack=False, scale_rew=False)) env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 with tf.Session(config=config) as sess: dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)) player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) # Other exploration schedules #eps_decay_sched = LinearTFSchedule(50000, 1.0, 0.01) #player = NStepPlayer(BatchedPlayer(env, EpsGreedyQNetwork(dqn.online_net, 0.1)), 3) #player = NStepPlayer(BatchedPlayer(env, EpsGreedyQNetwork(dqn.online_net, TFScheduleValue(sess, eps_decay_sched))), 3) #player = NStepPlayer(BatchedPlayer(env, SonicEpsGreedyQNetwork(dqn.online_net, TFScheduleValue(sess, eps_decay_sched))), 3) optimize = dqn.optimize(learning_rate=1e-4) sess.run(tf.global_variables_initializer()) reward_hist = [] total_steps = 0 def _handle_ep(steps, rew, env_rewards): nonlocal total_steps total_steps += steps reward_hist.append(rew) if total_steps % 10 == 0: print('%d episodes, %d steps: mean of last 100 episodes=%f' % (len(reward_hist), total_steps, sum(reward_hist[-100:]) / len(reward_hist[-100:]))) dqn.train( num_steps=2000000, # Make sure an exception arrives before we stop. player=player, replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, train_interval=1, target_interval=8192, batch_size=32, min_buffer_size=20000, tf_schedules=[eps_decay_sched], handle_ep=_handle_ep, restore_path='./pretrained_model', save_interval=None, )
def main(): """Run DQN until the environment throws an exception.""" env_fns, env_names = create_envs() env = BatchedFrameStack(batched_gym_env(env_fns), num_images=4, concat=False) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 with tf.Session(config=config) as sess: dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)) player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) optimize = dqn.optimize(learning_rate=1e-4) # Use ADAM sess.run(tf.global_variables_initializer()) reward_hist = [] total_steps = 0 def _handle_ep(steps, rew, env_rewards): nonlocal total_steps total_steps += steps reward_hist.append(rew) if total_steps % 1 == 0: print('%d episodes, %d steps: mean of last 100 episodes=%f' % (len(reward_hist), total_steps, sum(reward_hist[-100:]) / len(reward_hist[-100:]))) dqn.train( num_steps= 2000000000, # Make sure an exception arrives before we stop. player=player, replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, train_interval=1, target_interval=8192, batch_size=32, min_buffer_size=20000, handle_ep=_handle_ep, num_envs=len(env_fns), save_interval=10, )
def main(): """Run DQN until the environment throws an exception.""" # "results/rainbow/2/videos/6" env = make_env(stack=False, scale_rew=False, render=20, monitor=None, timelimit=False) # env = AllowBacktracking(make_env(stack=False, scale_rew=False)) # TODO we might not want to allow backtracking, it kinda hurts in mario env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 config.gpu_options.per_process_gpu_memory_fraction = 0.6 with tf.Session(config=config) as sess: dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)) # TODO rebuild the online_net form the saved model # type <anyrl.models.dqn_dist.NatureDistQNetwork object at ???> # important methods # model = dqn.online_net player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) with tf.device("/cpu"): # sess.run(tf.global_variables_initializer()) vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) try: for i in tqdm(range(100000)): trajectories = player.play() for trajectori in trajectories: trajectori pass except KeyboardInterrupt: env.close()
def main(): """Run DQN until the environment throws an exception.""" env = AllowBacktracking(make_env(stack=False, scale_rew=False)) env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 with tf.Session(config=config) as sess: dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)) player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) optimize = dqn.optimize(learning_rate=1e-4) sess.run(tf.global_variables_initializer()) saver = tf.train.Saver() saver.restore(sess, "/root/compo/model.ckpt") #print('model restored') replay_buffer = pickle.load( gzip.open('/root/compo/replay_buffer.p.gz', 'rb')) replay_buffer.alpha = 0.2 replay_buffer.beta = 0.4 replay_buffer.capacity = 100000 restore_ppo2_weights(sess) dqn.train( num_steps=2000000, # Make sure an exception arrives before we stop. player=player, replay_buffer= replay_buffer, #PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, train_interval=4, target_interval=8192, batch_size=32, min_buffer_size=20000)
def make_net(name): return rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)
def main(): """Run DQN until the environment throws an exception.""" print('creating env') env = AllowBacktracking(make_env(stack=False, scale_rew=False)) env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 print('starting tf session') with tf.Session(config=config) as sess: print('creating agent') online_net, target_net = rainbow_models(sess, env.action_space.n, gym_space_vectorizer( env.observation_space), min_val=-200, max_val=200) dqn = DQN(online_net, target_net) player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) optimize = dqn.optimize(learning_rate=1e-4) saver = tf.train.Saver() sess.run(tf.global_variables_initializer()) train_steps = 5000 print('training steps:', train_steps) for j in range(1): print(j) start = time.time() dqn.train( num_steps= train_steps, # Make sure an exception arrives before we stop. player=player, replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, train_interval=1, target_interval=8192, batch_size=32, min_buffer_size=10000) end = time.time() print(end - start) print('done training') print('save nn') save_path = saver.save(sess, "saved_models/rainbow5.ckpt") print("Model saved in path: %s" % save_path) tvars = tf.trainable_variables() tvars_vals = sess.run(tvars) #for var, val in zip(tvars, tvars_vals): # print(var.name, val[0]) #print(tvars_vals[0][-5:]) #print('stepping') #obs = env.reset() #online_net.step(obs, obs) '''
def main(): """ Entry-point for the program. """ args = _parse_args() # batched env = creates gym env, not sure what batched means # make_single_env = GrayscaleEnv > DownsampleEnv # GrayscaleEnv = turns RGB into grayscale # DownsampleEnv = down samples observation by N times where N is the specified variable (e.g. 2x smaller) env = batched_gym_env([partial(make_single_env, args.game)] * args.workers) env_test = make_single_env(args.game) #make_single_env(args.game) print('OBSSSS', env_test.observation_space) #env = CustomWrapper(args.game) # Using BatchedFrameStack with concat=False is more # memory efficient than other stacking options. env = BatchedFrameStack(env, num_images=4, concat=False) with tf.Session() as sess: def make_net(name): return rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200) dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)) player = BatchedPlayer(env, EpsGreedyQNetwork(dqn.online_net, args.epsilon)) optimize = dqn.optimize(learning_rate=args.lr) sess.run(tf.global_variables_initializer()) reward_hist = [] total_steps = 0 def _handle_ep(steps, rew): nonlocal total_steps total_steps += steps reward_hist.append(rew) if len(reward_hist) == REWARD_HISTORY: print('%d steps: mean=%f' % (total_steps, sum(reward_hist) / len(reward_hist))) reward_hist.clear() dqn.train(num_steps=int(1e7), player=player, replay_buffer=UniformReplayBuffer(args.buffer_size), optimize_op=optimize, target_interval=args.target_interval, batch_size=args.batch_size, min_buffer_size=args.min_buffer_size, handle_ep=_handle_ep) env.close()
def train(batched_env, env_count=1, batch_size_multiplier=32, num_steps=2000000, pretrained_model='artifacts/model/model.cpkt', output_dir='artifacts/model', use_schedules=True): """ Trains on a batched_env using anyrl-py's dqn and rainbow model. env_count: The number of envs in batched_env batch_size_multiplier: batch_size of the dqn train call will be env_count * batch_size_multiplier num_steps: The number of steps to run training for pretrained_model: Load tf weights from this model file output_dir: Save tf weights to this file use_schedules: Enables the tf_schedules for the train call. Schedules require internet access, so don't include on retro-contest evaluation server """ env = CollisionMapWrapper(batched_env) env = BatchedResizeImageWrapper(env) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 with tf.Session(config=config) as sess: dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)) scheduled_saver = ScheduledSaver(save_interval=10000, save_dir=output_dir) print('Outputting trained model to', output_dir) # Reporting uses BatchedPlayer to get _total_rewards batched_player = BatchedPlayer(env, dqn.online_net) player = NStepPlayer(batched_player, 3) optimize = dqn.optimize(learning_rate=1e-4) if pretrained_model is None: print('Initializing with random weights') sess.run(tf.global_variables_initializer()) else: print('Loading pre-trained model from', pretrained_model) scheduled_saver.saver.restore(sess, pretrained_model) print('Beginning Training, steps', num_steps) tf_schedules = [] if (use_schedules): tf_schedules = [ scheduled_saver, LosswiseSchedule(num_steps, batched_player), LoadingBar(num_steps) ] print(env_count * batch_size_multiplier) dqn.train( num_steps=num_steps, player=player, replay_buffer=PrioritizedReplayBuffer(300000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, train_interval=env_count, target_interval=8192, batch_size=env_count * batch_size_multiplier, min_buffer_size=max(4500, env_count * batch_size_multiplier), # min_buffer_size=60, tf_schedules=tf_schedules, handle_ep=print) scheduled_saver.save(sess)
#print(env.action_space.n) #StackedBox(84,84,1) config = tf.ConfigProto() config.gpu_options.allow_growth = True print('starting tf session') with tf.Session(config=config) as sess: print('creating agent') online_net, target_net = rainbow_models(sess, env.action_space.n, gym_space_vectorizer( env.observation_space), min_val=-200, max_val=200) saver = tf.train.Saver() sess.run(tf.global_variables_initializer()) saver.restore(sess, "saved_models/rainbow4.ckpt") print('model loaded') # RESET env.reset_start() obs = env.reset_wait() #print(obs1[0][0].shape)
def main(): """Run DQN until the environment throws an exception.""" config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 comm = MPI.COMM_WORLD # Use MPI for parallel evaluation rank = comm.Get_rank() size = comm.Get_size() env_fns, env_names = create_eval_envs() env = AllowBacktracking(env_fns[rank](stack=False, scale_rew=False)) env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) with tf.Session(config=config) as sess: dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)) player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) optimize = dqn.optimize(learning_rate=1e-4) sess.run(tf.global_variables_initializer()) reward_hist = [] total_steps = 0 def _handle_ep(steps, rew, env_rewards): nonlocal total_steps total_steps += steps reward_hist.append(rew) if total_steps % 1 == 0: avg_score = sum(reward_hist[-100:]) / len(reward_hist[-100:]) # Global Score global_score = np.zeros(1) local_score = np.array(avg_score) print("Local Score for " + env_names[rank] + " at episode " + str(len(reward_hist)) + " with timesteps: " + str(total_steps) + ": " + str(local_score)) comm.Allreduce(local_score, global_score, op=MPI.SUM) global_score /= size if rank == 0: print("Global Average Score at episode: " + str(len(reward_hist)) + ": " + str(global_score)) dqn.train( num_steps=2000000, # Make sure an exception arrives before we stop. player=player, replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, train_interval=1, target_interval=8192, batch_size=32, min_buffer_size=20000, handle_ep=_handle_ep, save_interval=None, restore_path= './checkpoints_rainbow/model-10' # Model to be evaluated )
def main(): parser = argparse.ArgumentParser() parser.add_argument('--restore', '-restore', action='store_true', help='restore from checkpoint file') parser.add_argument('--record', '-record', action='store_true', help='record bk2 movies') args = parser.parse_args() """Run DQN until the environment throws an exception.""" env = AllowBacktracking( make_env(stack=False, scale_rew=False, record=args.record)) env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) checkpoint_dir = os.path.join(os.getcwd(), 'results') results_dir = os.path.join(os.getcwd(), 'results', time.strftime("%d-%m-%Y_%H-%M-%S")) if not os.path.exists(results_dir): os.makedirs(results_dir) summary_writer = tf.summary.FileWriter(results_dir) # TODO # env = wrappers.Monitor(env, results_dir, force=True) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 with tf.Session(config=config) as sess: dqn = DQN(*rainbow_models(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), min_val=-200, max_val=200)) saver = tf.train.Saver() if args.restore: latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir) if latest_checkpoint: print("Loading model checkpoint {} ...\n".format( latest_checkpoint)) saver.restore(sess, latest_checkpoint) else: print("Checkpoint not found") player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) optimize = dqn.optimize(learning_rate=1e-4) sess.run(tf.global_variables_initializer()) reward_hist = [] total_steps = 0 # runs with every completed episode def _handle_ep(steps, rew): nonlocal total_steps total_steps += steps reward_hist.append(rew) summary_reward = tf.Summary() summary_reward.value.add(tag='global/reward', simple_value=rew) summary_writer.add_summary(summary_reward, global_step=total_steps) print('save model') saver.save(sess=sess, save_path=checkpoint_dir + '/model', global_step=total_steps) if len(reward_hist) == REWARD_HISTORY: print('%d steps: mean=%f' % (total_steps, sum(reward_hist) / len(reward_hist))) summary_meanreward = tf.Summary() summary_meanreward.value.add(tag='global/mean_reward', simple_value=sum(reward_hist) / len(reward_hist)) summary_writer.add_summary(summary_meanreward, global_step=total_steps) reward_hist.clear() dqn.train( num_steps=7000000, # Make sure an exception arrives before we stop. player=player, replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, train_interval=1, target_interval=8192, batch_size=32, min_buffer_size=20000, handle_ep=_handle_ep)