def make_env(process_idx, test): env = ale.ALE(args.rom, use_sdl=args.use_sdl, treat_life_lost_as_terminal=not test) if not test: misc.env_modifiers.make_reward_clipped(env, -1, 1) return env
def make_env(test): # Use different random seeds for train and test envs env_seed = 2 ** 31 - 1 - args.seed if test else args.seed env = ale.ALE(args.rom, use_sdl=args.use_sdl, treat_life_lost_as_terminal=not test, seed=env_seed) if not test: misc.env_modifiers.make_reward_clipped(env, -1, 1) return env
def test_reward(self): env = ale.ALE('pong') for _ in range(3): total_r = 0 while not env.is_terminal: a = random.randrange(len(env.legal_actions)) env.receive_action(a) total_r += env.reward self.assertGreater(total_r, -22) self.assertLess(total_r, -15) env.initialize()
def test_current_screen(self): env = ale.ALE('breakout') tempdir = tempfile.mkdtemp() print('tempdir: {}'.format(tempdir), file=sys.stderr) for episode in range(6): env.initialize() t = 0 while not env.is_terminal: for i in range(4): screen = env.state[i] self.assertEqual(screen.dtype, np.uint8) img = Image.fromarray(screen, mode='L') filename = '{}/{}_{}_{}.bmp'.format( tempdir, str(episode).zfill(6), str(t).zfill(6), i) img.save(filename) legal_actions = env.legal_actions a = random.randrange(len(legal_actions)) env.receive_action(a) t += 1
def test_episode(self): env = ale.ALE('breakout') self.assertFalse(env.is_terminal) last_state = env.state while not env.is_terminal: # test state self.assertEqual(len(env.state), 4) for s in env.state: self.assertEqual(s.shape, (84, 84)) self.assertEqual(s.dtype, np.uint8) print('state (sum)', sum(env.state).sum()) legal_actions = env.legal_actions print('legal_actions:', legal_actions) self.assertGreater(len(legal_actions), 0) a = random.randrange(len(legal_actions)) print('a', a) env.receive_action(a) if not env.is_terminal: np.testing.assert_array_equal( np.asarray(last_state[1:]), np.asarray(env.state[:3])) last_state = env.state
def main(): import logging logging.basicConfig(level=logging.DEBUG) parser = argparse.ArgumentParser() parser.add_argument('processes', type=int) parser.add_argument('rom', type=str) parser.add_argument('--seed', type=int, default=0, help='Random seed [0, 2 ** 31)') parser.add_argument('--outdir', type=str, default='results', help='Directory path to save output files.' ' If it does not exist, it will be created.') parser.add_argument('--use-sdl', action='store_true') parser.add_argument('--t-max', type=int, default=5) parser.add_argument('--replay-start-size', type=int, default=10000) parser.add_argument('--n-times-replay', type=int, default=4) parser.add_argument('--max-episode-len', type=int, default=10000) parser.add_argument('--beta', type=float, default=1e-2) parser.add_argument('--profile', action='store_true') parser.add_argument('--steps', type=int, default=8 * 10**7) parser.add_argument('--lr', type=float, default=7e-4) parser.add_argument('--eval-interval', type=int, default=10**6) parser.add_argument('--eval-n-runs', type=int, default=10) parser.add_argument('--weight-decay', type=float, default=0.0) parser.add_argument('--use-lstm', action='store_true') parser.add_argument('--demo', action='store_true', default=False) parser.add_argument('--load', type=str, default='') parser.set_defaults(use_sdl=False) parser.set_defaults(use_lstm=False) args = parser.parse_args() # Set a random seed used in ChainerRL. # If you use more than one processes, the results will be no longer # deterministic even with the same random seed. misc.set_random_seed(args.seed) # Set different random seeds for different subprocesses. # If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3]. # If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7]. process_seeds = np.arange(args.processes) + args.seed * args.processes assert process_seeds.max() < 2**31 args.outdir = experiments.prepare_output_dir(args, args.outdir) print('Output files are saved in {}'.format(args.outdir)) n_actions = ale.ALE(args.rom).number_of_actions if args.use_lstm: model = acer.ACERSharedModel( shared=links.Sequence(links.NIPSDQNHead(), L.LSTM(256, 256)), pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution), q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue), ) else: model = acer.ACERSharedModel( shared=links.NIPSDQNHead(), pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution), q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue), ) opt = rmsprop_async.RMSpropAsync(lr=7e-4, eps=4e-3, alpha=0.99) opt.setup(model) opt.add_hook(chainer.optimizer.GradientClipping(40)) if args.weight_decay > 0: opt.add_hook(NonbiasWeightDecay(args.weight_decay)) replay_buffer = EpisodicReplayBuffer(10**6 // args.processes) agent = acer.ACER(model, opt, t_max=args.t_max, gamma=0.99, replay_buffer=replay_buffer, n_times_replay=args.n_times_replay, replay_start_size=args.replay_start_size, beta=args.beta, phi=dqn_phi) if args.load: agent.load(args.load) def make_env(process_idx, test): # Use different random seeds for train and test envs process_seed = process_seeds[process_idx] env_seed = 2**31 - 1 - process_seed if test else process_seed env = ale.ALE(args.rom, use_sdl=args.use_sdl, treat_life_lost_as_terminal=not test, seed=env_seed) if not test: misc.env_modifiers.make_reward_clipped(env, -1, 1) return env if args.demo: env = make_env(0, True) eval_stats = experiments.eval_performance(env=env, agent=agent, n_runs=args.eval_n_runs) print('n_runs: {} mean: {} median: {} stdev {}'.format( args.eval_n_runs, eval_stats['mean'], eval_stats['median'], eval_stats['stdev'])) else: # Linearly decay the learning rate to zero def lr_setter(env, agent, value): agent.optimizer.lr = value lr_decay_hook = experiments.LinearInterpolationHook( args.steps, args.lr, 0, lr_setter) experiments.train_agent_async(agent=agent, outdir=args.outdir, processes=args.processes, make_env=make_env, profile=args.profile, steps=args.steps, eval_n_runs=args.eval_n_runs, eval_interval=args.eval_interval, max_episode_len=args.max_episode_len, global_step_hooks=[lr_decay_hook])
def test_seed(self): ale.ALE('breakout', seed=0) ale.ALE('breakout', seed=2 ** 31 - 1)
def test_state(self): env = ale.ALE('breakout') self.assertEqual(len(env.state), 4) for s in env.state: self.assertEqual(s.shape, (84, 84)) self.assertEqual(s.dtype, np.uint8)
def main(): # Prevent numpy from using multiple threads os.environ['OMP_NUM_THREADS'] = '1' import logging logging.basicConfig(level=logging.DEBUG) parser = argparse.ArgumentParser() parser.add_argument('rom', type=str) parser.add_argument('--gpu', type=int, default=0) parser.add_argument('--seed', type=int, default=0, help='Random seed [0, 2 ** 31)') parser.add_argument('--outdir', type=str, default=None) parser.add_argument('--use-sdl', action='store_true') parser.add_argument('--max-episode-len', type=int, default=10000) parser.add_argument('--profile', action='store_true') parser.add_argument('--steps', type=int, default=8 * 10**7) parser.add_argument('--lr', type=float, default=2.5e-4) parser.add_argument('--eval-interval', type=int, default=10**6) parser.add_argument('--eval-n-runs', type=int, default=10) parser.add_argument('--standardize-advantages', action='store_true') parser.add_argument('--weight-decay', type=float, default=0.0) parser.add_argument('--demo', action='store_true', default=False) parser.add_argument('--load', type=str, default='') # In the original paper, agent runs in 8 environments parallely # and samples 128 steps per environment. # Sample 128 * 8 steps, instead. parser.add_argument('--update-interval', type=int, default=128 * 8) parser.add_argument('--batchsize', type=int, default=32) parser.add_argument('--epochs', type=int, default=3) parser.set_defaults(use_sdl=False) args = parser.parse_args() # Set a random seed used in ChainerRL. misc.set_random_seed(args.seed, gpus=(args.gpu, )) args.outdir = experiments.prepare_output_dir(args, args.outdir) print('Output files are saved in {}'.format(args.outdir)) n_actions = ale.ALE(args.rom).number_of_actions model = A3CFF(n_actions) opt = chainer.optimizers.Adam(alpha=args.lr) opt.setup(model) opt.add_hook(chainer.optimizer.GradientClipping(40)) if args.weight_decay > 0: opt.add_hook(NonbiasWeightDecay(args.weight_decay)) agent = PPO( model, opt, gpu=args.gpu, phi=dqn_phi, update_interval=args.update_interval, minibatch_size=args.batchsize, epochs=args.epochs, clip_eps=0.1, clip_eps_vf=None, standardize_advantages=args.standardize_advantages, ) if args.load: agent.load(args.load) def make_env(test): # Use different random seeds for train and test envs env_seed = 2**31 - 1 - args.seed if test else args.seed env = ale.ALE(args.rom, use_sdl=args.use_sdl, treat_life_lost_as_terminal=not test, seed=env_seed) if not test: misc.env_modifiers.make_reward_clipped(env, -1, 1) return env if args.demo: env = make_env(True) eval_stats = experiments.eval_performance(env=env, agent=agent, n_runs=args.eval_n_runs) print('n_runs: {} mean: {} median: {} stdev: {}'.format( args.eval_n_runs, eval_stats['mean'], eval_stats['median'], eval_stats['stdev'])) else: # Linearly decay the learning rate to zero def lr_setter(env, agent, value): agent.optimizer.alpha = value lr_decay_hook = experiments.LinearInterpolationHook( args.steps, args.lr, 0, lr_setter) # Linearly decay the clipping parameter to zero def clip_eps_setter(env, agent, value): agent.clip_eps = value clip_eps_decay_hook = experiments.LinearInterpolationHook( args.steps, 0.1, 0, clip_eps_setter) experiments.train_agent_with_evaluation( agent=agent, env=make_env(False), eval_env=make_env(True), outdir=args.outdir, steps=args.steps, eval_n_runs=args.eval_n_runs, eval_interval=args.eval_interval, max_episode_len=args.max_episode_len, step_hooks=[ lr_decay_hook, clip_eps_decay_hook, ], )
def main(): import logging logging.basicConfig(level=logging.DEBUG) parser = argparse.ArgumentParser() parser.add_argument('processes', type=int) parser.add_argument('rom', type=str) parser.add_argument('--seed', type=int, default=0, help='Random seed [0, 2 ** 31)') parser.add_argument('--outdir', type=str, default='results', help='Directory path to save output files.' ' If it does not exist, it will be created.') parser.add_argument('--use-sdl', action='store_true') parser.add_argument('--t-max', type=int, default=5) parser.add_argument('--max-episode-len', type=int, default=10000) parser.add_argument('--beta', type=float, default=1e-2) parser.add_argument('--profile', action='store_true') parser.add_argument('--steps', type=int, default=8 * 10**7) parser.add_argument('--lr', type=float, default=7e-4) parser.add_argument('--eval-interval', type=int, default=10**6) parser.add_argument('--eval-n-runs', type=int, default=10) parser.add_argument('--weight-decay', type=float, default=0.0) parser.add_argument('--use-lstm', action='store_true') parser.add_argument('--demo', action='store_true', default=False) parser.add_argument('--load', type=str, default='') parser.set_defaults(use_sdl=False) parser.set_defaults(use_lstm=False) args = parser.parse_args() # Set a random seed used in ChainerRL. # If you use more than one processes, the results will be no longer # deterministic even with the same random seed. misc.set_random_seed(args.seed) # Set different random seeds for different subprocesses. # If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3]. # If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7]. process_seeds = np.arange(args.processes) + args.seed * args.processes assert process_seeds.max() < 2**31 args.outdir = experiments.prepare_output_dir(args, args.outdir) print('Output files are saved in {}'.format(args.outdir)) n_actions = ale.ALE(args.rom).number_of_actions if args.use_lstm: model = A3CLSTM(n_actions) else: model = A3CFF(n_actions) # Draw the computational graph and save it in the output directory. fake_obs = chainer.Variable(np.zeros((4, 84, 84), dtype=np.float32)[None], name='observation') with chainerrl.recurrent.state_reset(model): # The state of the model is reset again after drawing the graph chainerrl.misc.draw_computational_graph([model(fake_obs)], os.path.join( args.outdir, 'model')) opt = rmsprop_async.RMSpropAsync(lr=7e-4, eps=1e-1, alpha=0.99) opt.setup(model) opt.add_hook(chainer.optimizer.GradientClipping(40)) if args.weight_decay > 0: opt.add_hook(NonbiasWeightDecay(args.weight_decay)) agent = a3c.A3C(model, opt, t_max=args.t_max, gamma=0.99, beta=args.beta, phi=dqn_phi) if args.load: agent.load(args.load) def make_env(process_idx, test): # Use different random seeds for train and test envs process_seed = process_seeds[process_idx] env_seed = 2**31 - 1 - process_seed if test else process_seed env = ale.ALE(args.rom, use_sdl=args.use_sdl, treat_life_lost_as_terminal=not test, seed=env_seed) if not test: misc.env_modifiers.make_reward_clipped(env, -1, 1) return env if args.demo: env = make_env(0, True) eval_stats = experiments.eval_performance(env=env, agent=agent, n_runs=args.eval_n_runs) print('n_runs: {} mean: {} median: {} stdev: {}'.format( args.eval_n_runs, eval_stats['mean'], eval_stats['median'], eval_stats['stdev'])) else: # Linearly decay the learning rate to zero def lr_setter(env, agent, value): agent.optimizer.lr = value lr_decay_hook = experiments.LinearInterpolationHook( args.steps, args.lr, 0, lr_setter) experiments.train_agent_async(agent=agent, outdir=args.outdir, processes=args.processes, make_env=make_env, profile=args.profile, steps=args.steps, eval_n_runs=args.eval_n_runs, eval_interval=args.eval_interval, max_episode_len=args.max_episode_len, global_step_hooks=[lr_decay_hook])
def main(): parser = argparse.ArgumentParser() parser.add_argument('rom', type=str) parser.add_argument('--outdir', type=str, default='results', help='Directory path to save output files.' ' If it does not exist, it will be created.') parser.add_argument('--seed', type=int, default=0, help='Random seed [0, 2 ** 31)') parser.add_argument('--gpu', type=int, default=0) parser.add_argument('--demo', action='store_true', default=False) parser.add_argument('--load', type=str, default=None) parser.add_argument('--use-sdl', action='store_true', default=False) parser.add_argument('--final-exploration-frames', type=int, default=10**6) parser.add_argument('--final-epsilon', type=float, default=0.1) parser.add_argument('--eval-epsilon', type=float, default=0.05) parser.add_argument('--steps', type=int, default=10**7) parser.add_argument('--replay-start-size', type=int, default=5 * 10**4) parser.add_argument('--target-update-interval', type=int, default=10**4) parser.add_argument('--eval-interval', type=int, default=10**5) parser.add_argument('--update-interval', type=int, default=4) parser.add_argument('--eval-n-runs', type=int, default=10) parser.add_argument('--batch-size', type=int, default=32) parser.add_argument('--logging-level', type=int, default=20, help='Logging level. 10:DEBUG, 20:INFO etc.') args = parser.parse_args() import logging logging.basicConfig(level=args.logging_level) # Set a random seed used in ChainerRL. misc.set_random_seed(args.seed, gpus=(args.gpu, )) # Set different random seeds for train and test envs. train_seed = args.seed test_seed = 2**31 - 1 - args.seed args.outdir = experiments.prepare_output_dir(args, args.outdir) print('Output files are saved in {}'.format(args.outdir)) # In training, life loss is considered as terminal states env = ale.ALE(args.rom, use_sdl=args.use_sdl, seed=train_seed) misc.env_modifiers.make_reward_clipped(env, -1, 1) # In testing, an episode is terminated when all lives are lost eval_env = ale.ALE(args.rom, use_sdl=args.use_sdl, treat_life_lost_as_terminal=False, seed=test_seed) n_actions = env.number_of_actions n_atoms = 51 v_max = 10 v_min = -10 q_func = chainerrl.links.Sequence( chainerrl.links.NatureDQNHead(), chainerrl.q_functions.DistributionalFCStateQFunctionWithDiscreteAction( None, n_actions, n_atoms, v_min, v_max, n_hidden_channels=0, n_hidden_layers=0), ) # Draw the computational graph and save it in the output directory. chainerrl.misc.draw_computational_graph( [q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])], os.path.join(args.outdir, 'model')) # Use the same hyper parameters as https://arxiv.org/abs/1707.06887 opt = chainer.optimizers.Adam(2.5e-4, eps=1e-2 / args.batch_size) opt.setup(q_func) rbuf = replay_buffer.ReplayBuffer(10**6) explorer = explorers.LinearDecayEpsilonGreedy( 1.0, args.final_epsilon, args.final_exploration_frames, lambda: np.random.randint(n_actions)) agent = chainerrl.agents.CategoricalDQN( q_func, opt, rbuf, gpu=args.gpu, gamma=0.99, explorer=explorer, replay_start_size=args.replay_start_size, target_update_interval=args.target_update_interval, update_interval=args.update_interval, batch_accumulator='mean', phi=dqn_phi, ) if args.load: agent.load(args.load) if args.demo: eval_stats = experiments.eval_performance(env=eval_env, agent=agent, n_runs=args.eval_n_runs) print('n_runs: {} mean: {} median: {} stdev {}'.format( args.eval_n_runs, eval_stats['mean'], eval_stats['median'], eval_stats['stdev'])) else: # In testing DQN, randomly select 5% of actions eval_explorer = explorers.ConstantEpsilonGreedy( args.eval_epsilon, lambda: np.random.randint(n_actions)) experiments.train_agent_with_evaluation( agent=agent, env=env, steps=args.steps, eval_n_runs=args.eval_n_runs, eval_interval=args.eval_interval, outdir=args.outdir, eval_explorer=eval_explorer, save_best_so_far_agent=False, eval_env=eval_env)
def main(): # Prevent numpy from using multiple threads os.environ['OMP_NUM_THREADS'] = '1' import logging logging.basicConfig(level=logging.DEBUG) parser = argparse.ArgumentParser() parser.add_argument('processes', type=int) parser.add_argument('rom', type=str) parser.add_argument('--seed', type=int, default=None) parser.add_argument('--outdir', type=str, default=None) parser.add_argument('--use-sdl', action='store_true') parser.add_argument('--t-max', type=int, default=5) parser.add_argument('--max-episode-len', type=int, default=10000) parser.add_argument('--beta', type=float, default=1e-2) parser.add_argument('--profile', action='store_true') parser.add_argument('--steps', type=int, default=8 * 10**7) parser.add_argument('--lr', type=float, default=7e-4) parser.add_argument('--eval-interval', type=int, default=10**6) parser.add_argument('--eval-n-runs', type=int, default=10) parser.add_argument('--weight-decay', type=float, default=0.0) parser.add_argument('--use-lstm', action='store_true') parser.add_argument('--demo', action='store_true', default=False) parser.add_argument('--load', type=str, default='') parser.set_defaults(use_sdl=False) parser.set_defaults(use_lstm=False) args = parser.parse_args() if args.seed is not None: misc.set_random_seed(args.seed) args.outdir = experiments.prepare_output_dir(args, args.outdir) print('Output files are saved in {}'.format(args.outdir)) n_actions = ale.ALE(args.rom).number_of_actions if args.use_lstm: model = A3CLSTM(n_actions) else: model = A3CFF(n_actions) opt = rmsprop_async.RMSpropAsync(lr=7e-4, eps=1e-1, alpha=0.99) opt.setup(model) opt.add_hook(chainer.optimizer.GradientClipping(40)) if args.weight_decay > 0: opt.add_hook(NonbiasWeightDecay(args.weight_decay)) agent = a3c.A3C(model, opt, t_max=args.t_max, gamma=0.99, beta=args.beta, phi=dqn_phi) if args.load: agent.load(args.load) def make_env(process_idx, test): env = ale.ALE(args.rom, use_sdl=args.use_sdl, treat_life_lost_as_terminal=not test) if not test: misc.env_modifiers.make_reward_clipped(env, -1, 1) return env if args.demo: env = make_env(0, True) eval_stats = experiments.eval_performance(env=env, agent=agent, n_runs=args.eval_n_runs) print('n_runs: {} mean: {} median: {} stdev: {}'.format( args.eval_n_runs, eval_stats['mean'], eval_stats['median'], eval_stats['stdev'])) else: # Linearly decay the learning rate to zero def lr_setter(env, agent, value): agent.optimizer.lr = value lr_decay_hook = experiments.LinearInterpolationHook( args.steps, args.lr, 0, lr_setter) experiments.train_agent_async(agent=agent, outdir=args.outdir, processes=args.processes, make_env=make_env, profile=args.profile, steps=args.steps, eval_n_runs=args.eval_n_runs, eval_interval=args.eval_interval, max_episode_len=args.max_episode_len, global_step_hooks=[lr_decay_hook])
def main(): parser = argparse.ArgumentParser() parser.add_argument('rom', type=str) parser.add_argument('--outdir', type=str, default='results', help='Directory path to save output files.' ' If it does not exist, it will be created.') parser.add_argument('--seed', type=int, default=0, help='Random seed [0, 2 ** 31)') parser.add_argument('--gpu', type=int, default=0) parser.add_argument('--demo', action='store_true', default=False) parser.add_argument('--load', type=str, default=None) parser.add_argument('--use-sdl', action='store_true', default=False) parser.add_argument('--final-exploration-frames', type=int, default=10**6) parser.add_argument('--final-epsilon', type=float, default=0.1) parser.add_argument('--eval-epsilon', type=float, default=0.05) parser.add_argument('--arch', type=str, default='nature', choices=['nature', 'nips', 'dueling']) parser.add_argument('--steps', type=int, default=10**7) parser.add_argument('--replay-start-size', type=int, default=5 * 10**4) parser.add_argument('--target-update-interval', type=int, default=10**4) parser.add_argument('--eval-interval', type=int, default=10**5) parser.add_argument('--update-interval', type=int, default=4) parser.add_argument('--activation', type=str, default='relu') parser.add_argument('--eval-n-runs', type=int, default=10) parser.add_argument('--no-clip-delta', dest='clip_delta', action='store_false') parser.set_defaults(clip_delta=True) parser.add_argument('--agent', type=str, default='DQN', choices=['DQN', 'DoubleDQN', 'PAL']) parser.add_argument('--logging-level', type=int, default=20, help='Logging level. 10:DEBUG, 20:INFO etc.') args = parser.parse_args() import logging logging.basicConfig(level=args.logging_level) # Set a random seed used in ChainerRL. misc.set_random_seed(args.seed, gpus=(args.gpu, )) # Set different random seeds for train and test envs. train_seed = args.seed test_seed = 2**31 - 1 - args.seed args.outdir = experiments.prepare_output_dir(args, args.outdir) print('Output files are saved in {}'.format(args.outdir)) # In training, life loss is considered as terminal states env = ale.ALE(args.rom, use_sdl=args.use_sdl, seed=train_seed) misc.env_modifiers.make_reward_clipped(env, -1, 1) # In testing, an episode is terminated when all lives are lost eval_env = ale.ALE(args.rom, use_sdl=args.use_sdl, treat_life_lost_as_terminal=False, seed=test_seed) n_actions = env.number_of_actions activation = parse_activation(args.activation) q_func = parse_arch(args.arch, n_actions, activation) # Draw the computational graph and save it in the output directory. chainerrl.misc.draw_computational_graph( [q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])], os.path.join(args.outdir, 'model')) # Use the same hyper parameters as the Nature paper's opt = optimizers.RMSpropGraves(lr=2.5e-4, alpha=0.95, momentum=0.0, eps=1e-2) opt.setup(q_func) rbuf = replay_buffer.ReplayBuffer(10**6) explorer = explorers.LinearDecayEpsilonGreedy( 1.0, args.final_epsilon, args.final_exploration_frames, lambda: np.random.randint(n_actions)) Agent = parse_agent(args.agent) agent = Agent(q_func, opt, rbuf, gpu=args.gpu, gamma=0.99, explorer=explorer, replay_start_size=args.replay_start_size, target_update_interval=args.target_update_interval, clip_delta=args.clip_delta, update_interval=args.update_interval, batch_accumulator='sum', phi=dqn_phi) if args.load: agent.load(args.load) if args.demo: eval_stats = experiments.eval_performance(env=eval_env, agent=agent, n_runs=args.eval_n_runs) print('n_runs: {} mean: {} median: {} stdev {}'.format( args.eval_n_runs, eval_stats['mean'], eval_stats['median'], eval_stats['stdev'])) else: # In testing DQN, randomly select 5% of actions eval_explorer = explorers.ConstantEpsilonGreedy( args.eval_epsilon, lambda: np.random.randint(n_actions)) experiments.train_agent_with_evaluation( agent=agent, env=env, steps=args.steps, eval_n_runs=args.eval_n_runs, eval_interval=args.eval_interval, outdir=args.outdir, eval_explorer=eval_explorer, save_best_so_far_agent=False, eval_env=eval_env)
def main(): # Prevent numpy from using multiple threads os.environ['OMP_NUM_THREADS'] = '1' import logging logging.basicConfig(level=logging.DEBUG) parser = argparse.ArgumentParser() parser.add_argument('processes', type=int) parser.add_argument('rom', type=str) parser.add_argument('--seed', type=int, default=None) parser.add_argument('--outdir', type=str, default=None) parser.add_argument('--use-sdl', action='store_true') parser.add_argument('--t-max', type=int, default=5) parser.add_argument('--replay-start-size', type=int, default=10000) parser.add_argument('--n-times-replay', type=int, default=4) parser.add_argument('--max-episode-len', type=int, default=10000) parser.add_argument('--beta', type=float, default=1e-2) parser.add_argument('--profile', action='store_true') parser.add_argument('--steps', type=int, default=8 * 10**7) parser.add_argument('--lr', type=float, default=7e-4) parser.add_argument('--eval-frequency', type=int, default=10**6) parser.add_argument('--eval-n-runs', type=int, default=10) parser.add_argument('--weight-decay', type=float, default=0.0) parser.add_argument('--use-lstm', action='store_true') parser.add_argument('--demo', action='store_true', default=False) parser.add_argument('--load', type=str, default='') parser.set_defaults(use_sdl=False) parser.set_defaults(use_lstm=False) args = parser.parse_args() if args.seed is not None: misc.set_random_seed(args.seed) args.outdir = experiments.prepare_output_dir(args, args.outdir) print('Output files are saved in {}'.format(args.outdir)) n_actions = ale.ALE(args.rom).number_of_actions if args.use_lstm: model = acer.ACERSharedModel( shared=links.Sequence(links.NIPSDQNHead(), L.LSTM(256, 256)), pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution), q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue), ) else: model = acer.ACERSharedModel( shared=links.NIPSDQNHead(), pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution), q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue), ) opt = rmsprop_async.RMSpropAsync(lr=7e-4, eps=4e-3, alpha=0.99) opt.setup(model) opt.add_hook(chainer.optimizer.GradientClipping(40)) if args.weight_decay > 0: opt.add_hook(NonbiasWeightDecay(args.weight_decay)) replay_buffer = EpisodicReplayBuffer(10**6 // args.processes) agent = acer.ACER(model, opt, t_max=args.t_max, gamma=0.99, replay_buffer=replay_buffer, n_times_replay=args.n_times_replay, replay_start_size=args.replay_start_size, beta=args.beta, phi=dqn_phi) if args.load: agent.load(args.load) def make_env(process_idx, test): env = ale.ALE(args.rom, use_sdl=args.use_sdl, treat_life_lost_as_terminal=not test) if not test: misc.env_modifiers.make_reward_clipped(env, -1, 1) return env if args.demo: env = make_env(0, True) mean, median, stdev = experiments.eval_performance( env=env, agent=agent, n_runs=args.eval_n_runs) print('n_runs: {} mean: {} median: {} stdev'.format( args.eval_n_runs, mean, median, stdev)) else: experiments.train_agent_async(agent=agent, outdir=args.outdir, processes=args.processes, make_env=make_env, profile=args.profile, steps=args.steps, eval_n_runs=args.eval_n_runs, eval_frequency=args.eval_frequency, max_episode_len=args.max_episode_len)
def main(): import logging logging.basicConfig(level=logging.DEBUG) parser = argparse.ArgumentParser() parser.add_argument('rom', type=str) parser.add_argument('--outdir', type=str, default=None) parser.add_argument('--seed', type=int, default=None) parser.add_argument('--gpu', type=int, default=0) parser.add_argument('--demo', action='store_true', default=False) parser.add_argument('--load', type=str, default=None) parser.add_argument('--use-sdl', action='store_true', default=False) parser.add_argument('--final-exploration-frames', type=int, default=10 ** 6) parser.add_argument('--model', type=str, default='') parser.add_argument('--arch', type=str, default='nature', choices=['nature', 'nips', 'dueling']) parser.add_argument('--steps', type=int, default=10 ** 7) parser.add_argument('--replay-start-size', type=int, default=5 * 10 ** 4) parser.add_argument('--target-update-frequency', type=int, default=10 ** 4) parser.add_argument('--eval-frequency', type=int, default=10 ** 5) parser.add_argument('--update-frequency', type=int, default=4) parser.add_argument('--activation', type=str, default='relu') parser.add_argument('--eval-n-runs', type=int, default=10) parser.add_argument('--no-clip-delta', dest='clip_delta', action='store_false') parser.set_defaults(clip_delta=True) parser.add_argument('--agent', type=str, default='DQN', choices=['DQN', 'DoubleDQN', 'PAL']) args = parser.parse_args() if args.seed is not None: misc.set_random_seed(args.seed) args.outdir = experiments.prepare_output_dir(args, args.outdir) print('Output files are saved in {}'.format(args.outdir)) # In training, life loss is considered as terminal states env = ale.ALE(args.rom, use_sdl=args.use_sdl) misc.env_modifiers.make_reward_clipped(env, -1, 1) # In testing, an episode is terminated when all lives are lost eval_env = ale.ALE(args.rom, use_sdl=args.use_sdl, treat_life_lost_as_terminal=False) n_actions = env.number_of_actions activation = parse_activation(args.activation) q_func = parse_arch(args.arch, n_actions, activation) # Use the same hyper parameters as the Nature paper's opt = optimizers.RMSpropGraves( lr=2.5e-4, alpha=0.95, momentum=0.0, eps=1e-2) opt.setup(q_func) rbuf = replay_buffer.ReplayBuffer(10 ** 6) explorer = explorers.LinearDecayEpsilonGreedy( 1.0, 0.1, args.final_exploration_frames, lambda: np.random.randint(n_actions)) Agent = parse_agent(args.agent) agent = Agent(q_func, opt, rbuf, gpu=args.gpu, gamma=0.99, explorer=explorer, replay_start_size=args.replay_start_size, target_update_frequency=args.target_update_frequency, clip_delta=args.clip_delta, update_frequency=args.update_frequency, batch_accumulator='sum', phi=dqn_phi) if args.load: agent.load(args.load) if args.demo: mean, median, stdev = experiments.eval_performance( env=eval_env, agent=agent, n_runs=args.eval_n_runs) print('n_runs: {} mean: {} median: {} stdev'.format( args.eval_n_runs, mean, median, stdev)) else: # In testing DQN, randomly select 5% of actions eval_explorer = explorers.ConstantEpsilonGreedy( 5e-2, lambda: np.random.randint(n_actions)) experiments.train_agent_with_evaluation( agent=agent, env=env, steps=args.steps, eval_n_runs=args.eval_n_runs, eval_frequency=args.eval_frequency, outdir=args.outdir, eval_explorer=eval_explorer, eval_env=eval_env)