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 = 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.""" 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(): """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(): 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(): """ Entry-point for the program. """ env = gym.make('CartPole-v0') with tf.Session() as sess: make_net = lambda name: MLPQNetwork(sess, env.action_space.n, gym_space_vectorizer( env.observation_space), name, layer_sizes=[32]) dqn = DQN(make_net('online'), make_net('target')) player = BasicPlayer(env, EpsGreedyQNetwork(dqn.online_net, EPSILON), batch_size=STEPS_PER_UPDATE) optimize = dqn.optimize(learning_rate=LEARNING_RATE) sess.run(tf.global_variables_initializer()) dqn.train(num_steps=30000, player=player, replay_buffer=UniformReplayBuffer(BUFFER_SIZE), optimize_op=optimize, target_interval=200, batch_size=64, min_buffer_size=200, handle_ep=lambda _, rew: print('got reward: ' + str(rew))) 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) """ 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(): env_name = 'MineRLNavigateDense-v0' """Run DQN until the environment throws an exception.""" base_env = [SimpleNavigateEnvWrapper(get_env(env_name)) for _ in range(1)] env = BatchedFrameStack(BatchedGymEnv([base_env]), num_images=4, concat=True) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 with tf.Session(config=config) as sess: online, target = mine_rainbow_online_target(mine_cnn, sess, env.action_space.n, gym_space_vectorizer( env.observation_space), min_val=-200, max_val=200) dqn = DQN(online, target) player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) optimize = dqn.optimize(learning_rate=1e-4) sess.run(tf.global_variables_initializer()) buffer_capacity = 5000 replay_buffer = PrioritizedReplayBuffer(buffer_capacity, 0.5, 0.4, epsilon=0.1) iter = non_bugged_data_arr(env_name, num_trajs=100) expert_player = NStepPlayer(ImitationPlayer(iter, 200), 3) for traj in expert_player.play(): replay_buffer.add_sample(traj, init_weight=1) print('starting training') dqn.train(num_steps=200, player=player, replay_buffer=replay_buffer, optimize_op=optimize, train_interval=1, target_interval=8192, batch_size=32, min_buffer_size=20000) print('starting eval') player._cur_states = None score = evaluate(player) print(score)
def main(): """ Entry-point for the program. """ args = _parse_args() env = batched_gym_env([partial(make_single_env, args.game)] * args.workers) # 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 NatureQNetwork(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), name, dueling=True) dqn = DQN(make_net('online'), make_net('target')) 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 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.""" 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 main(): if local_env: # Select Random Level if local from retro_contest.local import make levels = [ 'SpringYardZone.Act3', 'SpringYardZone.Act2', 'GreenHillZone.Act3', 'GreenHillZone.Act1', 'StarLightZone.Act2', 'StarLightZone.Act1', 'MarbleZone.Act2', 'MarbleZone.Act1', 'MarbleZone.Act3', 'ScrapBrainZone.Act2', 'LabyrinthZone.Act2', 'LabyrinthZone.Act1', 'LabyrinthZone.Act3' ] level_choice = levels[random.randrange(0, 13, 1)] env = make(game='SonicTheHedgehog-Genesis', state=level_choice) else: print('connecting to remote environment') env = grc.RemoteEnv('tmp/sock') print('starting episode') env = TrackedEnv(env) solutions = env.solutions # Track Solutions state_size = env.observation_space action_size = env.action_space.n print(state_size, action_size) env.assist = False env.trainer = False # Begin with mentor led exploration env.resume_rl(True) # Begin with RL exploration env.reset() while env.total_steps_ever <= TOTAL_TIMESTEPS: # Interact with Retro environment until Total TimeSteps expire. while env.trainer: print('Entering Self Play') keys = getch() if keys == 'A': env.control(-1) if keys == 'B': env.control(4) if keys == 'C': env.control(3) if keys == 'D': env.control(2) if keys == 'rr': env.trainer = False continue if keys == ' ': env.close() env = make(game='SonicTheHedgehog-Genesis', state=levels[random.randrange(0, 13, 1)]) env = TrackedEnv(env) env.reset() # Initialize Gaming Environment env.trainer = True if env.steps > 1: print('Prev Rew', env.step_rew_history[-1], 'Curr_Loc', env.reward_history[-1], 'Med Rew', np.median(env.step_rew_history[-3:])) if env.episode % RL_PLAY_PCT == 0: tf.reset_default_graph() with tf.Session() as sess: def make_net(name): return MLPQNetwork(sess, env.action_space.n, gym_space_vectorizer( env.observation_space), name, layer_sizes=[32]) dqn = DQN(make_net('online'), make_net('target')) bplayer = BasicPlayer(env, EpsGreedyQNetwork( dqn.online_net, EPSILON), batch_size=STEPS_PER_UPDATE) optimize = dqn.optimize(learning_rate=LEARNING_RATE) sess.run(tf.global_variables_initializer()) env.agent = 'DQN' dqn.train( num_steps=TRAINING_STEPS, player=bplayer, replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, target_interval=200, batch_size=64, min_buffer_size=200, handle_ep=lambda _, rew: print('Exited DQN with : ' + str( rew) + str(env.steps))) new_ep = True # New Episode Flag while new_ep: if new_ep: if (solutions and random.random() < EXPLOIT_BIAS + env.total_steps_ever / TOTAL_TIMESTEPS): solutions = sorted(solutions, key=lambda x: np.mean(x[0])) best_pair = solutions[-1] new_rew = exploit(env, best_pair[1]) best_pair[0].append(new_rew) print('replayed best with reward %f' % new_rew) print(best_pair[0]) continue else: env.reset() new_ep = False env.agent = 'JERK' rew, new_ep = move(env, 100) if not new_ep and rew <= 0: #print('backtracking due to negative reward: %f' % rew) _, new_ep = move(env, 70, left=True) if new_ep: solutions.append( ([max(env.reward_history)], env.best_sequence()))
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()
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) 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) env.close() optimize = dqn.optimize(learning_rate=1e-4) saver = tf.train.Saver() sess.run(tf.global_variables_initializer()) train_steps = 1000 #200000 for i in range(3): stage = random.choice(stages) game = random.choice(games) print('creating env') env = AllowBacktracking( make_env_multi(game, stage, stack=False, scale_rew=False))
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)
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(): if local_env: # Select Random Level if local levels = ['SpringYardZone.Act3', 'SpringYardZone.Act2', 'GreenHillZone.Act3', 'GreenHillZone.Act1', 'StarLightZone.Act2', 'StarLightZone.Act1', 'MarbleZone.Act2', 'MarbleZone.Act1', 'MarbleZone.Act3', 'ScrapBrainZone.Act2', 'LabyrinthZone.Act2', 'LabyrinthZone.Act1', 'LabyrinthZone.Act3'] level_choice = random.randrange(0, 13, 1) env = make_env(stack=True, scale_rew=False, local=local_env, level_choice=level_choice) #-3 else: print('connecting to remote environment') env = grc.RemoteEnv('tmp/sock') print('starting episode') env = AllowBacktracking(env) solutions = env.solutions # Track Solutions state_size = env.observation_space action_size = env.action_space.n print(state_size, action_size) env.assist = False env.trainer = train # Begin with mentor led exploration env.reset() while env.total_steps_ever <= TOTAL_TIMESTEPS: # Interact with Retro environment until Total TimeSteps expire. while env.trainer: print('Entering Self Play') keys = getch() if keys == 'A': env.control(-1) if keys == 'B': env.control(4) if keys == 'C': env.control(3) if keys == 'D': env.control(2) buttons = ["B", "A", "MODE", "START", "UP", "DOWN", "LEFT", "RIGHT", "C", "Y", "X", "Z"] actions = [['LEFT'], ['RIGHT'], ['LEFT', 'DOWN'], ['RIGHT', 'DOWN'], ['DOWN'], ['DOWN', 'B'], ['B']] if keys == 'rr': env.trainer = False continue if keys == ' ': env.close() env = make_env(stack=False, scale_rew=False, local=local_env) env = AllowBacktracking(env) env.reset() # Initialize Gaming Environment env.trainer = True if env.episode % RL_PLAY_PCT == 0: tf.reset_default_graph() with tf.Session() as sess: def make_net(name): return MLPQNetwork(sess, env.action_space.n, gym_space_vectorizer(env.observation_space), name, layer_sizes=[32]) dqn = DQN(make_net('online'), make_net('target')) bplayer = BasicPlayer(env, EpsGreedyQNetwork(dqn.online_net, EPSILON), batch_size=STEPS_PER_UPDATE) optimize = dqn.optimize(learning_rate=LEARNING_RATE) sess.run(tf.global_variables_initializer()) env.agent = 'DQN' dqn.train(num_steps=TRAINING_STEPS, player=bplayer, replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1), optimize_op=optimize, target_interval=200, batch_size=64, min_buffer_size=200, handle_ep=lambda _, rew: print('Exited DQN with : ' + str(rew) + str(env.steps))) new_ep = True # New Episode Flag while new_ep: if new_ep: if (solutions and random.random() < EXPLOIT_BIAS + env.total_steps_ever / TOTAL_TIMESTEPS): new_state, new_rew, done = env.spawn() continue else: env.reset() new_ep = False env.agent = 'JERK' rew, new_ep = move(env, 100) if not new_ep and rew <= 0: #print('backtracking due to negative reward: %f' % rew) _, new_ep = move(env, 70, left=True) if new_ep: solutions.append(([max(env.reward_history)], env.best_sequence()))
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)
def main(): """Run DQN until the environment throws an exception.""" #env = AllowBacktracking(make_env(stack=False, scale_rew=False)) #envs = make_training_envs() #env = BatchedFrameStack(BatchedGymEnv(envs), num_images=4, concat=False) #env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False) envs = get_training_envs() game, state = random.choice(envs) env = make_training_env(game, state, stack=False, scale_rew=False) env = prep_env(env) config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 with tf.Session(config=config) as sess: dqn = DQN(*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) loss = dqn.loss train_writer = tf.summary.FileWriter('./logs/multiple/train', sess.graph) tf.summary.scalar("loss", loss) reward = tf.Variable(0., name='reward', trainable=False) tf.summary.scalar('reward', tf.reduce_mean(reward)) steps = tf.Variable(0, name='steps', trainable=False) tf.summary.scalar('steps', tf.reduce_mean(steps)) summary_op = tf.summary.merge_all() sess.run(tf.global_variables_initializer()) print(tf.trainable_variables()) #graph = tf.get_default_graph() #restore_saver = tf.train.Saver({ # 'dense1/bias': graph.get_tensor_by_name('online/dense1/bias:0'), # 'dense1/kernel': graph.get_tensor_by_name('online/dense1/kernel:0'), # 'layer_1/bias': graph.get_tensor_by_name('online/layer_1/bias:0'), # 'layer_1/kernel': graph.get_tensor_by_name('online/layer_1/kernel:0'), # 'layer_2/bias': graph.get_tensor_by_name('online/layer_2/bias:0'), # 'layer_2/kernel': graph.get_tensor_by_name('online/layer_2/kernel:0'), # 'layer_3/bias': graph.get_tensor_by_name('online/layer_3/bias:0'), # 'layer_3/kernel': graph.get_tensor_by_name('online/layer_3/kernel:0'), # 'dense1/bias': graph.get_tensor_by_name('online_1/dense1/bias:0'), # 'dense1/kernel': graph.get_tensor_by_name('online_1/dense1/kernel:0'), # 'layer_1/bias': graph.get_tensor_by_name('online_1/layer_1/bias:0'), # 'layer_1/kernel': graph.get_tensor_by_name('online_1/layer_1/kernel:0'), # 'layer_2/bias': graph.get_tensor_by_name('online_1/layer_2/bias:0'), # 'layer_2/kernel': graph.get_tensor_by_name('online_1/layer_2/kernel:0'), # 'layer_3/bias': graph.get_tensor_by_name('online_1/layer_3/bias:0'), # 'layer_3/kernel': graph.get_tensor_by_name('online_1/layer_3/kernel:0'), # 'dense1/bias': graph.get_tensor_by_name('online_2/dense1/bias:0'), # 'dense1/kernel': graph.get_tensor_by_name('online_2/dense1/kernel:0'), # 'layer_1/bias': graph.get_tensor_by_name('online_2/layer_1/bias:0'), # 'layer_1/kernel': graph.get_tensor_by_name('online_2/layer_1/kernel:0'), # 'layer_2/bias': graph.get_tensor_by_name('online_2/layer_2/bias:0'), # 'layer_2/kernel': graph.get_tensor_by_name('online_2/layer_2/kernel:0'), # 'layer_3/bias': graph.get_tensor_by_name('online_2/layer_3/bias:0'), # 'layer_3/kernel': graph.get_tensor_by_name('online_2/layer_3/kernel:0'), # 'dense1/bias': graph.get_tensor_by_name('target/dense1/bias:0'), # 'dense1/kernel': graph.get_tensor_by_name('target/dense1/kernel:0'), # 'layer_1/bias': graph.get_tensor_by_name('target/layer_1/bias:0'), # 'layer_1/kernel': graph.get_tensor_by_name('target/layer_1/kernel:0'), # 'layer_2/bias': graph.get_tensor_by_name('target/layer_2/bias:0'), # 'layer_2/kernel': graph.get_tensor_by_name('target/layer_2/kernel:0'), # 'layer_3/bias': graph.get_tensor_by_name('target/layer_3/bias:0'), # 'layer_3/kernel': graph.get_tensor_by_name('target/layer_3/kernel:0'), # }) #restore_saver.restore(sess, './model-images/model.ckpt') #print('model restored') weights = joblib.load('./ppo2_weights_266.joblib') #[<tf.Variable 'model/c1/w:0' shape=(8, 8, 4, 32) dtype=float32_ref>, <tf.Variable 'model/c1/b:0' shape=(1, 32, 1, 1) dtype=float32_ref>, <tf.Variable 'model/c2/w:0' shape=(4, 4, 32, 64) dtype=float32_ref>, <tf.Variable 'model/c2/b:0' shape=(1, 64, 1, 1) dtype=float32_ref>, <tf.Variable 'model/c3/w:0' shape=(3, 3, 64, 64) dtype=float32_ref>, <tf.Variable 'model/c3/b:0' shape=(1, 64, 1, 1) dtype=float32_ref>, <tf.Variable 'model/fc1/w:0' shape=(3136, 512) dtype=float32_ref>, <tf.Variable 'model/fc1/b:0' shape=(512,) dtype=float32_ref>, <tf.Variable 'model/v/w:0' shape=(512, 1) dtype=float32_ref>, <tf.Variable 'model/v/b:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'model/pi/w:0' shape=(512, 7) dtype=float32_ref>, <tf.Variable 'model/pi/b:0' shape=(7,) dtype=float32_ref>] graph = tf.get_default_graph() for model in ['online', 'target']: tensor_names = [ '{}/layer_1/conv2d/kernel:0', '{}/layer_1/conv2d/bias:0', '{}/layer_2/conv2d/kernel:0', '{}/layer_2/conv2d/bias:0', '{}/layer_3/conv2d/kernel:0', '{}/layer_3/conv2d/bias:0', #'{}/dense1/kernel:0', #'{}/dense1/bias:0' ] for i in range(len(tensor_names)): tensor_name = tensor_names[i].format(model) tensor = graph.get_tensor_by_name(tensor_name) weight = weights[i] if 'bias' in tensor_name: weight = np.reshape(weight, tensor.get_shape()) print('about to assign {} value with size {}'.format( tensor_name, weights[i].shape)) sess.run(tf.assign(tensor, weight)) saver = tf.train.Saver() save_path = saver.save(sess, "./model/model.ckpt") print('Saved model') replay_buffer = PrioritizedReplayBuffer(100000, 0.5, 0.4, epsilon=0.1) #replay_buffer = pickle.load(gzip.open('./docker-build/model/replay_buffer.p.gz', 'rb')) #replay_buffer = pickle.load(open('./model/replay_buffer.p', 'rb')) total_steps = 50000000 steps_per_env = 5000 env.close() for i in range(int(total_steps / steps_per_env)): game, state = random.choice(envs) env = make_training_env(game, state, stack=False, scale_rew=False) env = prep_env(env) player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) #dqn.train(num_steps=steps_per_env, # Make sure an exception arrives before we stop. # player=player, # replay_buffer=replay_buffer, # optimize_op=optimize, # train_interval=1, # target_interval=8192, # batch_size=32, # min_buffer_size=20000) summary = train( dqn, num_steps= steps_per_env, # Make sure an exception arrives before we stop. player=player, replay_buffer=replay_buffer, optimize_op=optimize, train_interval=4, target_interval=8192, batch_size=32, min_buffer_size=20000, summary_op=summary_op, handle_ep=lambda st, rew: (reward.assign(rew), steps.assign(st)), handle_step=lambda st, rew: (reward.assign(reward + rew), steps.assign(steps + st))) env.close() if summary: train_writer.add_summary(summary, i) else: print('No summary') save_path = saver.save(sess, "./model/model.ckpt") pickle.dump(replay_buffer, open("./model/replay_buffer.p", "wb")) print('Saved model')
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(): discount = os.environ.get('RETRO_DISCOUNT') if discount != None: discount = float(discount) else: discount = 0.99 print("DISCOUNT: %s" % (discount, )) """Run DQN until the environment throws an exception.""" config = tf.ConfigProto() config.gpu_options.allow_growth = True # pylint: disable=E1101 config.log_device_placement = False with tf.Session(config=config) as sess: state_encoder = StateEncoder(sess) env = make_batched_env() env_ids = env.env_ids env = BatchedFrameStack(env, num_images=4, concat=True) env.env_ids = env_ids env = ExplorationBatchedEnv(env, Exploration, state_encoder=state_encoder) if 'RETRO_POLICY_DIR' in os.environ: expert = PolicyExpert(sess, batch_size=1, policy_dir=os.environ['RETRO_POLICY_DIR']) elif not 'RETRO_NOEXPERT' in os.environ: expert = RandomMoveExpert() else: expert = None if os.environ['RETRO_DQN'] == 'soft_noisy_net': dqn = DQN(*soft_noisy_net_models( sess, env.action_space.n, gym_space_vectorizer(env.observation_space), discount=discount, #0.99 expert=expert)) elif os.environ['RETRO_DQN'] == 'soft_rainbow': dqn = DQN(*soft_rainbow_models( sess, env.action_space.n, gym_space_vectorizer(env.observation_space), num_atoms=101, min_val=-1000, #-200 max_val=1000, #200 discount=discount, #0.99 expert=expert)) if "RETRO_CHECKPOINT_DIR" in os.environ: scheduler_saver = ScheduledSaver( sess, os.environ["RETRO_CHECKPOINT_DIR"] + "/tensorflow/") else: scheduler_saver = None player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3) optimize = dqn.optimize(learning_rate=1e-4) sess.run(tf.global_variables_initializer()) if 'RETRO_INIT_DIR' in os.environ: saver = tf.train.Saver(var_list=list( filter( lambda v: not 'sigma' in v.name and not 'dqn_model/noisy_layer_1' in v.name and not 'dqn_model/noisy_layer_2' in v.name, tf.trainable_variables('^dqn_model/')))) latest_checkpoint = tf.train.latest_checkpoint( os.environ['RETRO_INIT_DIR']) print("DQN_INIT_CHECKPOINT: %s" % (latest_checkpoint, )) saver.restore(sess, latest_checkpoint) #from tensorflow.python.tools import inspect_checkpoint as chkp #chkp.print_tensors_in_checkpoint_file(latest_checkpoint,'',all_tensors=True) state_encoder.initialize() if expert: expert.initialize() replay_buffer = PrioritizedReplayBuffer(int( os.environ.get("RETRO_DQN_BUFFER_SIZE", 250000)), 0.5, 0.4, epsilon=0.1) dqn.train( num_steps=1000000, # Make sure an exception arrives before we stop. player=player, replay_buffer=replay_buffer, optimize_op=optimize, train_interval=1, target_interval=int( os.environ.get("RETRO_DQN_TARGET_INTERVAL", 8192)), batch_size=32, min_buffer_size=int( os.environ.get('RETRO_DQN_MIN_BUFFER_SIZE', 20000)), handle_ep=lambda steps, rew: scheduler_saver.handle_episode(steps) if scheduler_saver is not None else None)