def _test_load_rainbow(self, gpu): from pfrl.q_functions import DistributionalDuelingDQN q_func = DistributionalDuelingDQN(4, 51, -10, 10) pnn.to_factorized_noisy(q_func, sigma_scale=0.5) explorer = explorers.Greedy() opt = torch.optim.Adam(q_func.parameters(), 6.25e-5, eps=1.5 * 10**-4) rbuf = replay_buffers.ReplayBuffer(100) agent = agents.CategoricalDoubleDQN( q_func, opt, rbuf, gpu=gpu, gamma=0.99, explorer=explorer, minibatch_size=32, replay_start_size=50, target_update_interval=32000, update_interval=4, batch_accumulator="mean", phi=lambda x: x, ) downloaded_model, exists = download_model( "Rainbow", "BreakoutNoFrameskip-v4", model_type=self.pretrained_type) agent.load(downloaded_model) if os.environ.get("PFRL_ASSERT_DOWNLOADED_MODEL_IS_CACHED"): assert exists
def _test_load_td3(self, gpu): obs_size = 11 action_size = 3 def make_q_func_with_optimizer(): q_func = nn.Sequential( pnn.ConcatObsAndAction(), nn.Linear(obs_size + action_size, 400), nn.ReLU(), nn.Linear(400, 300), nn.ReLU(), nn.Linear(300, 1), ) q_func_optimizer = torch.optim.Adam(q_func.parameters()) return q_func, q_func_optimizer q_func1, q_func1_optimizer = make_q_func_with_optimizer() q_func2, q_func2_optimizer = make_q_func_with_optimizer() policy = nn.Sequential( nn.Linear(obs_size, 400), nn.ReLU(), nn.Linear(400, 300), nn.ReLU(), nn.Linear(300, action_size), nn.Tanh(), pfrl.policies.DeterministicHead(), ) policy_optimizer = torch.optim.Adam(policy.parameters()) rbuf = replay_buffers.ReplayBuffer(100) explorer = explorers.AdditiveGaussian(scale=0.1, low=[-1.0, -1.0, -1.0], high=[1.0, 1.0, 1.0]) agent = agents.TD3( policy, q_func1, q_func2, policy_optimizer, q_func1_optimizer, q_func2_optimizer, rbuf, gamma=0.99, soft_update_tau=5e-3, explorer=explorer, replay_start_size=1000, gpu=gpu, minibatch_size=100, burnin_action_func=None, ) downloaded_model, exists = download_model( "TD3", "Hopper-v2", model_type=self.pretrained_type) agent.load(downloaded_model) if os.environ.get("PFRL_ASSERT_DOWNLOADED_MODEL_IS_CACHED"): assert exists
def _test_load_ddpg(self, gpu): obs_size = 11 action_size = 3 from pfrl.nn import ConcatObsAndAction q_func = nn.Sequential( ConcatObsAndAction(), nn.Linear(obs_size + action_size, 400), nn.ReLU(), nn.Linear(400, 300), nn.ReLU(), nn.Linear(300, 1), ) from pfrl.nn import BoundByTanh from pfrl.policies import DeterministicHead policy = nn.Sequential( nn.Linear(obs_size, 400), nn.ReLU(), nn.Linear(400, 300), nn.ReLU(), nn.Linear(300, action_size), BoundByTanh(low=[-1.0, -1.0, -1.0], high=[1.0, 1.0, 1.0]), DeterministicHead(), ) opt_a = torch.optim.Adam(policy.parameters()) opt_c = torch.optim.Adam(q_func.parameters()) explorer = explorers.AdditiveGaussian(scale=0.1, low=[-1.0, -1.0, -1.0], high=[1.0, 1.0, 1.0]) agent = agents.DDPG( policy, q_func, opt_a, opt_c, replay_buffers.ReplayBuffer(100), gamma=0.99, explorer=explorer, replay_start_size=1000, target_update_method="soft", target_update_interval=1, update_interval=1, soft_update_tau=5e-3, n_times_update=1, gpu=gpu, minibatch_size=100, burnin_action_func=None, ) downloaded_model, exists = download_model( "DDPG", "Hopper-v2", model_type=self.pretrained_type) agent.load(downloaded_model) if os.environ.get("PFRL_ASSERT_DOWNLOADED_MODEL_IS_CACHED"): assert exists
def _test_load_iqn(self, gpu): n_actions = 4 q_func = pfrl.agents.iqn.ImplicitQuantileQFunction( psi=nn.Sequential( nn.Conv2d(4, 32, 8, stride=4), nn.ReLU(), nn.Conv2d(32, 64, 4, stride=2), nn.ReLU(), nn.Conv2d(64, 64, 3, stride=1), nn.ReLU(), nn.Flatten(), ), phi=nn.Sequential( pfrl.agents.iqn.CosineBasisLinear(64, 3136), nn.ReLU(), ), f=nn.Sequential( nn.Linear(3136, 512), nn.ReLU(), nn.Linear(512, n_actions), ), ) # Use the same hyper parameters as https://arxiv.org/abs/1710.10044 opt = torch.optim.Adam(q_func.parameters(), lr=5e-5, eps=1e-2 / 32) rbuf = replay_buffers.ReplayBuffer(100) explorer = explorers.LinearDecayEpsilonGreedy( start_epsilon=1.0, end_epsilon=0.1, decay_steps=10**6, random_action_func=lambda: np.random.randint(4), ) agent = agents.IQN( q_func, opt, rbuf, gpu=gpu, gamma=0.99, explorer=explorer, replay_start_size=50, target_update_interval=10**4, update_interval=4, batch_accumulator="mean", phi=lambda x: x, quantile_thresholds_N=64, quantile_thresholds_N_prime=64, quantile_thresholds_K=32, ) downloaded_model, exists = download_model( "IQN", "BreakoutNoFrameskip-v4", model_type=self.pretrained_type) agent.load(downloaded_model) if os.environ.get("PFRL_ASSERT_DOWNLOADED_MODEL_IS_CACHED"): assert exists
def _test_load_ppo(self, gpu): obs_size = 11 action_size = 3 from pfrl.policies import GaussianHeadWithStateIndependentCovariance policy = torch.nn.Sequential( nn.Linear(obs_size, 64), nn.Tanh(), nn.Linear(64, 64), nn.Tanh(), nn.Linear(64, action_size), GaussianHeadWithStateIndependentCovariance( action_size=action_size, var_type="diagonal", var_func=lambda x: torch.exp(2 * x), # Parameterize log std var_param_init=0, # log std = 0 => std = 1 ), ) vf = torch.nn.Sequential( nn.Linear(obs_size, 64), nn.Tanh(), nn.Linear(64, 64), nn.Tanh(), nn.Linear(64, 1), ) model = pnn.Branched(policy, vf) opt = torch.optim.Adam(model.parameters(), lr=3e-4, eps=1e-5) agent = agents.PPO( model, opt, obs_normalizer=None, gpu=gpu, update_interval=2048, minibatch_size=64, epochs=10, clip_eps_vf=None, entropy_coef=0, standardize_advantages=True, gamma=0.995, lambd=0.97, ) downloaded_model, exists = download_model("PPO", "Hopper-v2", model_type="final") agent.load(downloaded_model) if os.environ.get("PFRL_ASSERT_DOWNLOADED_MODEL_IS_CACHED"): assert exists
def _test_load_dqn(self, gpu): from pfrl.q_functions import DiscreteActionValueHead n_actions = 4 q_func = nn.Sequential( pnn.LargeAtariCNN(), init_chainer_default(nn.Linear(512, n_actions)), DiscreteActionValueHead(), ) # Use the same hyperparameters as the Nature paper opt = pfrl.optimizers.RMSpropEpsInsideSqrt( q_func.parameters(), lr=2.5e-4, alpha=0.95, momentum=0.0, eps=1e-2, centered=True, ) rbuf = replay_buffers.ReplayBuffer(100) explorer = explorers.LinearDecayEpsilonGreedy( start_epsilon=1.0, end_epsilon=0.1, decay_steps=10**6, random_action_func=lambda: np.random.randint(4), ) agent = agents.DQN( q_func, opt, rbuf, gpu=gpu, gamma=0.99, explorer=explorer, replay_start_size=50, target_update_interval=10**4, clip_delta=True, update_interval=4, batch_accumulator="sum", phi=lambda x: x, ) downloaded_model, exists = download_model( "DQN", "BreakoutNoFrameskip-v4", model_type=self.pretrained_type) agent.load(downloaded_model) if os.environ.get("PFRL_ASSERT_DOWNLOADED_MODEL_IS_CACHED"): assert exists
def _test_load_trpo(self, gpu): obs_size = 11 action_size = 3 policy = torch.nn.Sequential( nn.Linear(obs_size, 64), nn.Tanh(), nn.Linear(64, 64), nn.Tanh(), nn.Linear(64, action_size), pfrl.policies.GaussianHeadWithStateIndependentCovariance( action_size=action_size, var_type="diagonal", var_func=lambda x: torch.exp(2 * x), # Parameterize log std var_param_init=0, # log std = 0 => std = 1 ), ) vf = torch.nn.Sequential( nn.Linear(obs_size, 64), nn.Tanh(), nn.Linear(64, 64), nn.Tanh(), nn.Linear(64, 1), ) vf_opt = torch.optim.Adam(vf.parameters()) agent = agents.TRPO( policy=policy, vf=vf, vf_optimizer=vf_opt, gpu=gpu, update_interval=5000, max_kl=0.01, conjugate_gradient_max_iter=20, conjugate_gradient_damping=1e-1, gamma=0.995, lambd=0.97, vf_epochs=5, entropy_coef=0, ) downloaded_model, exists = download_model( "TRPO", "Hopper-v2", model_type=self.pretrained_type) agent.load(downloaded_model) if os.environ.get("PFRL_ASSERT_DOWNLOADED_MODEL_IS_CACHED"): assert exists
def test_load_a3c(self): from pfrl.policies import SoftmaxCategoricalHead obs_size = 4 n_actions = 4 a3c_model = nn.Sequential( nn.Conv2d(obs_size, 16, 8, stride=4), nn.ReLU(), nn.Conv2d(16, 32, 4, stride=2), nn.ReLU(), nn.Flatten(), nn.Linear(2592, 256), nn.ReLU(), pfrl.nn.Branched( nn.Sequential( nn.Linear(256, n_actions), SoftmaxCategoricalHead(), ), nn.Linear(256, 1), ), ) from pfrl.optimizers import SharedRMSpropEpsInsideSqrt opt = SharedRMSpropEpsInsideSqrt(a3c_model.parameters(), lr=7e-4, eps=1e-1, alpha=0.99) agent = agents.A3C(a3c_model, opt, t_max=5, gamma=0.99, beta=1e-2, phi=lambda x: x) downloaded_model, exists = download_model( "A3C", "BreakoutNoFrameskip-v4", model_type=self.pretrained_type) agent.load(downloaded_model) if os.environ.get("PFRL_ASSERT_DOWNLOADED_MODEL_IS_CACHED"): assert exists
def _test_load_sac(self, gpu): obs_size = 11 action_size = 3 def squashed_diagonal_gaussian_head(x): assert x.shape[-1] == action_size * 2 mean, log_scale = torch.chunk(x, 2, dim=1) log_scale = torch.clamp(log_scale, -20.0, 2.0) var = torch.exp(log_scale * 2) from torch import distributions base_distribution = distributions.Independent( distributions.Normal(loc=mean, scale=torch.sqrt(var)), 1) # cache_size=1 is required for numerical stability return distributions.transformed_distribution.TransformedDistribution( base_distribution, [distributions.transforms.TanhTransform(cache_size=1)], ) from pfrl.nn.lmbda import Lambda policy = nn.Sequential( nn.Linear(obs_size, 256), nn.ReLU(), nn.Linear(256, 256), nn.ReLU(), nn.Linear(256, action_size * 2), Lambda(squashed_diagonal_gaussian_head), ) policy_optimizer = torch.optim.Adam(policy.parameters(), lr=3e-4) def make_q_func_with_optimizer(): q_func = nn.Sequential( pfrl.nn.ConcatObsAndAction(), nn.Linear(obs_size + action_size, 256), nn.ReLU(), nn.Linear(256, 256), nn.ReLU(), nn.Linear(256, 1), ) torch.nn.init.xavier_uniform_(q_func[1].weight) torch.nn.init.xavier_uniform_(q_func[3].weight) torch.nn.init.xavier_uniform_(q_func[5].weight) q_func_optimizer = torch.optim.Adam(q_func.parameters(), lr=3e-4) return q_func, q_func_optimizer q_func1, q_func1_optimizer = make_q_func_with_optimizer() q_func2, q_func2_optimizer = make_q_func_with_optimizer() agent = agents.SoftActorCritic( policy, q_func1, q_func2, policy_optimizer, q_func1_optimizer, q_func2_optimizer, replay_buffers.ReplayBuffer(100), gamma=0.99, replay_start_size=1000, gpu=gpu, minibatch_size=256, burnin_action_func=None, entropy_target=-3, temperature_optimizer_lr=3e-4, ) downloaded_model, exists = download_model( "SAC", "Hopper-v2", model_type=self.pretrained_type) agent.load(downloaded_model) if os.environ.get("PFRL_ASSERT_DOWNLOADED_MODEL_IS_CACHED"): assert exists
def main(): import logging torch.cuda.empty_cache() parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=int, default=0, help='GPU to use, set to -1 if no GPU') parser.add_argument('--env', type=str, default='LidarBat-v0', help='Bat simulation env') parser.add_argument('--arch', type=str, default='FFGaussian', choices=('FFSoftmax', 'FFMellowmax', 'FFGaussian')) parser.add_argument('--bound-mean', action='store_true') parser.add_argument('--seed', type=int, default=0, help='Random seed [0, 2 ** 32)') parser.add_argument('--outdir', type=str, default='data/ppo', help='Directory path to save output files.' ' If it does not exist, it will be created.') parser.add_argument('--steps', type=int, default=10**6) parser.add_argument('--eval-interval', type=int, default=10000) parser.add_argument('--eval-n-runs', type=int, default=10) parser.add_argument('--reward-scale-factor', type=float, default=1e-2) parser.add_argument('--standardize-advantages', action='store_true') parser.add_argument('--render', action='store_true', default=False) parser.add_argument('--lr', type=float, default=3e-4) 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='') parser.add_argument("--load-pretrained", action="store_true", default=False) parser.add_argument('--logger-level', type=int, default=logging.DEBUG) parser.add_argument('--monitor', action='store_true') parser.add_argument( "--log-interval", type=int, default=1000, help= "Interval in timesteps between outputting log messages during training", ) parser.add_argument("--num-envs", type=int, default=1, help="Number of envs run in parallel.") parser.add_argument("--batch-size", type=int, default=64, help="Minibatch size") parser.add_argument('--update-interval', type=int, default=2048) parser.add_argument('--batchsize', type=int, default=64) parser.add_argument('--epochs', type=int, default=10) parser.add_argument('--entropy-coef', type=float, default=0.0) args = parser.parse_args() logging.basicConfig(level=args.logger_level) # Set a random seed used in PFRL utils.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.num_envs) + args.seed * args.num_envs assert process_seeds.max() < 2**32 args.outdir = experiments.prepare_output_dir(args, args.outdir) def make_env(process_idx, test): env = gym.make(args.env) # Use different random seeds for train and test envs process_seed = int(process_seeds[process_idx]) env_seed = 2**32 - 1 - process_seed if test else process_seed env.seed(env_seed) # Cast observations to float32 because our model uses float32 env = pfrl.wrappers.CastObservationToFloat32(env) if args.monitor: env = pfrl.wrappers.Monitor(env, args.outdir) # TODO # if not test is not here if args.render: env = pfrl.wrappers.Render(env) return env def make_batch_env(test): return pfrl.envs.MultiprocessVectorEnv([ functools.partial(make_env, idx, test) for idx, env in enumerate(range(args.num_envs)) ]) # Only for getting timesteps, and obs-action spaces sample_env = gym.make(args.env) timestep_limit = sample_env.spec.max_episode_steps obs_space = sample_env.observation_space action_space = sample_env.action_space print("Observation space:", obs_space) print("Action space:", action_space) assert isinstance(action_space, gym.spaces.Box) # Normalize observations based on their empirical mean and variance obs_normalizer = pfrl.nn.EmpiricalNormalization(obs_space.low.size, clip_threshold=5) # pulicy here magic number must be concidered again obs_size = obs_space.low.size action_size = action_space.low.size policy = torch.nn.Sequential( nn.Linear(obs_size, 64), nn.Tanh(), nn.Linear(64, 64), nn.Tanh(), nn.Linear(64, action_size), pfrl.policies.GaussianHeadWithStateIndependentCovariance( action_size=action_size, var_type="diagonal", var_func=lambda x: torch.exp(2 * x), # Parameterize log std var_param_init=0, # log std = 0 => std = 1 ), ) vf = torch.nn.Sequential( nn.Linear(obs_size, 64), nn.Tanh(), nn.Linear(64, 64), nn.Tanh(), nn.Linear(64, 1), ) # While the original paper initialized weights by normal distribution, # we use orthogonal initialization as the latest openai/baselines does. def ortho_init(layer, gain): nn.init.orthogonal_(layer.weight, gain=gain) nn.init.zeros_(layer.bias) ortho_init(policy[0], gain=1) ortho_init(policy[2], gain=1) ortho_init(policy[4], gain=1e-2) ortho_init(vf[0], gain=1) ortho_init(vf[2], gain=1) ortho_init(vf[4], gain=1) # Combine a policy and a value function into a single model model = pfrl.nn.Branched(policy, vf) opt = torch.optim.Adam(model.parameters(), lr=args.lr, eps=1e-5) agent = PPO( model, opt, obs_normalizer=obs_normalizer, gpu=args.gpu, update_interval=args.update_interval, minibatch_size=args.batch_size, epochs=args.epochs, clip_eps_vf=None, entropy_coef=args.entropy_coef, standardize_advantages=True, gamma=0.995, lambd=0.97, ) if args.load or args.load_pretrained: if args.load_pretrained: raise Exception("Pretrained models are currently unsupported.") # either load or load_pretrained must be false assert not args.load or not args.load_pretrained if args.load: agent.load(args.load) else: agent.load( utils.download_model("PPO", args.env, model_type="final")[0]) if args.demo: env = make_batch_env(True) eval_stats = experiments.eval_performance( env=env, agent=agent, n_steps=None, n_episodes=args.eval_n_runs, max_episode_len=timestep_limit, ) print("n_runs: {} mean: {} median: {} stdev {}".format( args.eval_n_runs, eval_stats["mean"], eval_stats["median"], eval_stats["stdev"], )) else: experiments.train_agent_batch_with_evaluation( agent=agent, env=make_batch_env(False), eval_env=make_batch_env(True), outdir=args.outdir, steps=args.steps, eval_n_steps=None, eval_n_episodes=args.eval_n_runs, eval_interval=args.eval_interval, log_interval=args.log_interval, max_episode_len=timestep_limit, save_best_so_far_agent=False, )
def main(): parser = argparse.ArgumentParser() 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( "--env", type=str, default="Hopper-v2", help="OpenAI Gym MuJoCo env to perform algorithm on.", ) parser.add_argument("--seed", type=int, default=0, help="Random seed [0, 2 ** 32)") parser.add_argument("--gpu", type=int, default=0, help="GPU to use, set to -1 if no GPU.") parser.add_argument("--load", type=str, default="", help="Directory to load agent from.") parser.add_argument( "--steps", type=int, default=10**6, help="Total number of timesteps to train the agent.", ) parser.add_argument( "--eval-n-runs", type=int, default=10, help="Number of episodes run for each evaluation.", ) parser.add_argument( "--eval-interval", type=int, default=5000, help="Interval in timesteps between evaluations.", ) parser.add_argument( "--replay-start-size", type=int, default=10000, help="Minimum replay buffer size before " + "performing gradient updates.", ) parser.add_argument("--batch-size", type=int, default=100, help="Minibatch size") parser.add_argument("--render", action="store_true", help="Render env states in a GUI window.") parser.add_argument("--demo", action="store_true", help="Just run evaluation, not training.") parser.add_argument("--load-pretrained", action="store_true", default=False) parser.add_argument("--pretrained-type", type=str, default="best", choices=["best", "final"]) parser.add_argument("--monitor", action="store_true", help="Wrap env with gym.wrappers.Monitor.") parser.add_argument("--log-level", type=int, default=logging.INFO, help="Level of the root logger.") args = parser.parse_args() logging.basicConfig(level=args.log_level) args.outdir = experiments.prepare_output_dir(args, args.outdir, argv=sys.argv) print("Output files are saved in {}".format(args.outdir)) # Set a random seed used in PFRL utils.set_random_seed(args.seed) def make_env(test): env = gym.make(args.env) # Unwrap TimeLimit wrapper assert isinstance(env, gym.wrappers.TimeLimit) env = env.env # Use different random seeds for train and test envs env_seed = 2**32 - 1 - args.seed if test else args.seed env.seed(env_seed) # Cast observations to float32 because our model uses float32 env = pfrl.wrappers.CastObservationToFloat32(env) if args.monitor: env = pfrl.wrappers.Monitor(env, args.outdir) if args.render and not test: env = pfrl.wrappers.Render(env) return env env = make_env(test=False) timestep_limit = env.spec.max_episode_steps obs_space = env.observation_space action_space = env.action_space print("Observation space:", obs_space) print("Action space:", action_space) obs_size = obs_space.low.size action_size = action_space.low.size policy = nn.Sequential( nn.Linear(obs_size, 400), nn.ReLU(), nn.Linear(400, 300), nn.ReLU(), nn.Linear(300, action_size), nn.Tanh(), pfrl.policies.DeterministicHead(), ) policy_optimizer = torch.optim.Adam(policy.parameters()) def make_q_func_with_optimizer(): q_func = nn.Sequential( pfrl.nn.ConcatObsAndAction(), nn.Linear(obs_size + action_size, 400), nn.ReLU(), nn.Linear(400, 300), nn.ReLU(), nn.Linear(300, 1), ) q_func_optimizer = torch.optim.Adam(q_func.parameters()) return q_func, q_func_optimizer q_func1, q_func1_optimizer = make_q_func_with_optimizer() q_func2, q_func2_optimizer = make_q_func_with_optimizer() rbuf = replay_buffers.ReplayBuffer(10**6) explorer = explorers.AdditiveGaussian(scale=0.1, low=action_space.low, high=action_space.high) def burnin_action_func(): """Select random actions until model is updated one or more times.""" return np.random.uniform(action_space.low, action_space.high).astype(np.float32) # Hyperparameters in http://arxiv.org/abs/1802.09477 agent = pfrl.agents.TD3( policy, q_func1, q_func2, policy_optimizer, q_func1_optimizer, q_func2_optimizer, rbuf, gamma=0.99, soft_update_tau=5e-3, explorer=explorer, replay_start_size=args.replay_start_size, gpu=args.gpu, minibatch_size=args.batch_size, burnin_action_func=burnin_action_func, ) if len(args.load) > 0 or args.load_pretrained: # either load or load_pretrained must be false assert not len(args.load) > 0 or not args.load_pretrained if len(args.load) > 0: agent.load(args.load) else: agent.load( utils.download_model("TD3", args.env, model_type=args.pretrained_type)[0]) eval_env = make_env(test=True) if args.demo: eval_stats = experiments.eval_performance( env=eval_env, agent=agent, n_steps=None, n_episodes=args.eval_n_runs, max_episode_len=timestep_limit, ) print("n_runs: {} mean: {} median: {} stdev {}".format( args.eval_n_runs, eval_stats["mean"], eval_stats["median"], eval_stats["stdev"], )) import json import os with open(os.path.join(args.outdir, "demo_scores.json"), "w") as f: json.dump(eval_stats, f) else: experiments.train_agent_with_evaluation( agent=agent, env=env, steps=args.steps, eval_env=eval_env, eval_n_steps=None, eval_n_episodes=args.eval_n_runs, eval_interval=args.eval_interval, outdir=args.outdir, train_max_episode_len=timestep_limit, )
def main(): parser = argparse.ArgumentParser() 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( "--env", type=str, default="Hopper-v2", help="OpenAI Gym MuJoCo env to perform algorithm on.", ) parser.add_argument("--num-envs", type=int, default=1, help="Number of envs run in parallel.") parser.add_argument("--seed", type=int, default=0, help="Random seed [0, 2 ** 32)") parser.add_argument("--gpu", type=int, default=0, help="GPU to use, set to -1 if no GPU.") parser.add_argument("--load", type=str, default="", help="Directory to load agent from.") parser.add_argument( "--steps", type=int, default=10**6, help="Total number of timesteps to train the agent.", ) parser.add_argument( "--eval-n-runs", type=int, default=10, help="Number of episodes run for each evaluation.", ) parser.add_argument( "--eval-interval", type=int, default=5000, help="Interval in timesteps between evaluations.", ) parser.add_argument( "--replay-start-size", type=int, default=10000, help="Minimum replay buffer size before " + "performing gradient updates.", ) parser.add_argument("--batch-size", type=int, default=256, help="Minibatch size") parser.add_argument("--render", action="store_true", help="Render env states in a GUI window.") parser.add_argument("--demo", action="store_true", help="Just run evaluation, not training.") parser.add_argument("--load-pretrained", action="store_true", default=False) parser.add_argument("--pretrained-type", type=str, default="best", choices=["best", "final"]) parser.add_argument("--monitor", action="store_true", help="Wrap env with gym.wrappers.Monitor.") parser.add_argument( "--log-interval", type=int, default=1000, help= "Interval in timesteps between outputting log messages during training", ) parser.add_argument("--log-level", type=int, default=logging.INFO, help="Level of the root logger.") parser.add_argument( "--policy-output-scale", type=float, default=1.0, help="Weight initialization scale of policy output.", ) parser.add_argument( "--optimizer", type=str, default="AdaBelief", ) args = parser.parse_args() logging.basicConfig(level=args.log_level) args.outdir = experiments.prepare_output_dir(args, args.outdir, argv=sys.argv) print("Output files are saved in {}".format(args.outdir)) # Set a random seed used in PFRL utils.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.num_envs) + args.seed * args.num_envs assert process_seeds.max() < 2**32 def make_env(process_idx, test): env = gym.make(args.env) # Unwrap TimiLimit wrapper assert isinstance(env, gym.wrappers.TimeLimit) env = env.env # Use different random seeds for train and test envs process_seed = int(process_seeds[process_idx]) env_seed = 2**32 - 1 - process_seed if test else process_seed env.seed(env_seed) # Cast observations to float32 because our model uses float32 env = pfrl.wrappers.CastObservationToFloat32(env) # Normalize action space to [-1, 1]^n env = pfrl.wrappers.NormalizeActionSpace(env) if args.monitor: env = gym.wrappers.Monitor(env, args.outdir) if args.render: env = pfrl.wrappers.Render(env) return env def make_batch_env(test): return pfrl.envs.MultiprocessVectorEnv([ functools.partial(make_env, idx, test) for idx, env in enumerate(range(args.num_envs)) ]) sample_env = make_env(process_idx=0, test=False) timestep_limit = sample_env.spec.max_episode_steps obs_space = sample_env.observation_space action_space = sample_env.action_space print("Observation space:", obs_space) print("Action space:", action_space) obs_size = obs_space.low.size action_size = action_space.low.size if LooseVersion(torch.__version__) < LooseVersion("1.5.0"): raise Exception("This script requires a PyTorch version >= 1.5.0") def squashed_diagonal_gaussian_head(x): assert x.shape[-1] == action_size * 2 mean, log_scale = torch.chunk(x, 2, dim=1) log_scale = torch.clamp(log_scale, -20.0, 2.0) var = torch.exp(log_scale * 2) base_distribution = distributions.Independent( distributions.Normal(loc=mean, scale=torch.sqrt(var)), 1) # cache_size=1 is required for numerical stability return distributions.transformed_distribution.TransformedDistribution( base_distribution, [distributions.transforms.TanhTransform(cache_size=1)]) def make_optimizer(parameters): if args.optimizer == "OfficialAdaBelief": import adabelief_pytorch optim_class = adabelief_pytorch.AdaBelief optim = optim_class(parameters, betas=(0.9, 0.999), eps=1e-12) else: optim_class = getattr( torch_optimizer, args.optimizer, getattr(torch.optim, args.optimizer, None), ) optim = optim_class(parameters) assert optim_class is not None print(str(optim_class), "with default hyperparameters") return optim policy = nn.Sequential( nn.Linear(obs_size, 256), nn.ReLU(), nn.Linear(256, 256), nn.ReLU(), nn.Linear(256, action_size * 2), Lambda(squashed_diagonal_gaussian_head), ) torch.nn.init.xavier_uniform_(policy[0].weight) torch.nn.init.xavier_uniform_(policy[2].weight) torch.nn.init.xavier_uniform_(policy[4].weight, gain=args.policy_output_scale) policy_optimizer = make_optimizer(policy.parameters()) def make_q_func_with_optimizer(): q_func = nn.Sequential( pfrl.nn.ConcatObsAndAction(), nn.Linear(obs_size + action_size, 256), nn.ReLU(), nn.Linear(256, 256), nn.ReLU(), nn.Linear(256, 1), ) torch.nn.init.xavier_uniform_(q_func[1].weight) torch.nn.init.xavier_uniform_(q_func[3].weight) torch.nn.init.xavier_uniform_(q_func[5].weight) q_func_optimizer = make_optimizer(q_func.parameters()) return q_func, q_func_optimizer q_func1, q_func1_optimizer = make_q_func_with_optimizer() q_func2, q_func2_optimizer = make_q_func_with_optimizer() rbuf = replay_buffers.ReplayBuffer(10**6) def burnin_action_func(): """Select random actions until model is updated one or more times.""" return np.random.uniform(action_space.low, action_space.high).astype(np.float32) # Hyperparameters in http://arxiv.org/abs/1802.09477 agent = pfrl.agents.SoftActorCritic( policy, q_func1, q_func2, policy_optimizer, q_func1_optimizer, q_func2_optimizer, rbuf, gamma=0.99, replay_start_size=args.replay_start_size, gpu=args.gpu, minibatch_size=args.batch_size, burnin_action_func=burnin_action_func, entropy_target=-action_size, temperature_optimizer_lr=3e-4, ) if len(args.load) > 0 or args.load_pretrained: if args.load_pretrained: raise Exception("Pretrained models are currently unsupported.") # either load or load_pretrained must be false assert not len(args.load) > 0 or not args.load_pretrained if len(args.load) > 0: agent.load(args.load) else: agent.load( utils.download_model("SAC", args.env, model_type=args.pretrained_type)[0]) if args.demo: eval_stats = experiments.eval_performance( env=make_batch_env(test=True), agent=agent, n_steps=None, n_episodes=args.eval_n_runs, max_episode_len=timestep_limit, ) print("n_runs: {} mean: {} median: {} stdev {}".format( args.eval_n_runs, eval_stats["mean"], eval_stats["median"], eval_stats["stdev"], )) else: experiments.train_agent_batch_with_evaluation( agent=agent, env=make_batch_env(test=False), eval_env=make_batch_env(test=True), outdir=args.outdir, steps=args.steps, eval_n_steps=None, eval_n_episodes=args.eval_n_runs, eval_interval=args.eval_interval, log_interval=args.log_interval, max_episode_len=timestep_limit, )
def main(): import logging parser = argparse.ArgumentParser() parser.add_argument( "--gpu", type=int, default=0, help="GPU to use, set to -1 if no GPU." ) parser.add_argument( "--env", type=str, default="reach_target-ee-vision-v0", help="OpenAI Gym MuJoCo env to perform algorithm on.", ) parser.add_argument( "--num-envs", type=int, default=1, help="Number of envs run in parallel." ) parser.add_argument("--seed", type=int, default=0, help="Random seed [0, 2 ** 32)") 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( "--steps", type=int, default=2 * 10 ** 6, help="Total number of timesteps to train the agent.", ) parser.add_argument( "--eval-interval", type=int, default=100000, help="Interval in timesteps between evaluations.", ) parser.add_argument( "--eval-n-runs", type=int, default=100, help="Number of episodes run for each evaluation.", ) parser.add_argument( "--render", action="store_true", help="Render env states in a GUI window." ) parser.add_argument( "--demo", action="store_true", help="Just run evaluation, not training." ) parser.add_argument("--load-pretrained", action="store_true", default=False) parser.add_argument( "--load", type=str, default="", help="Directory to load agent from." ) parser.add_argument( "--log-level", type=int, default=logging.INFO, help="Level of the root logger." ) parser.add_argument( "--monitor", action="store_true", help="Wrap env with gym.wrappers.Monitor." ) parser.add_argument( "--log-interval", type=int, default=1000, help="Interval in timesteps between outputting log messages during training", ) parser.add_argument( "--update-interval", type=int, default=2048, help="Interval in timesteps between model updates.", ) parser.add_argument( "--epochs", type=int, default=10, help="Number of epochs to update model for per PPO iteration.", ) parser.add_argument( "--action-size", type=int, default=3, help="Action size (needs to match env.action_space)", ) parser.add_argument("--batch-size", type=int, default=64, help="Minibatch size") args = parser.parse_args() logging.basicConfig(level=args.log_level) # Set a random seed used in PFRL utils.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.num_envs) + args.seed * args.num_envs assert process_seeds.max() < 2 ** 32 args.outdir = experiments.prepare_output_dir(args, args.outdir) def make_env(process_idx, test): render_mode = 'human' if args.render else None env = NormalizeAction(GraspActionWrapper(FlattenObservation(ResizeObservation(WristObsWrapper(gym.make(args.env, render_mode=render_mode)), (64, 64))), args.action_size)) # env = GraspActionWrapper(RescaleAction(FlattenObservation(ResizeObservation(WristObsWrapper(gym.make(args.env)), (64, 64))), -0.5, 0.5)) # Use different random seeds for train and test envs process_seed = int(process_seeds[process_idx]) env_seed = 2 ** 32 - 1 - process_seed if test else process_seed env.seed(env_seed) # Cast observations to float32 because our model uses float32 env = pfrl.wrappers.CastObservationToFloat32(env) if args.monitor: env = pfrl.wrappers.Monitor(env, args.outdir) if args.render: env = pfrl.wrappers.Render(env) return env def make_batch_env(test): return MultiprocessVectorEnv( [ functools.partial(make_env, idx, test) for idx, env in enumerate(range(args.num_envs)) ] ) # Only for getting timesteps, and obs-action spaces # sample_env = RescaleAction(GraspActionWrapper(FlattenObservation(ResizeObservation(WristObsWrapper(gym.make(args.env)), (64, 64))), args.action_size), -0.5, 0.5) # timestep_limit = sample_env.spec.max_episode_steps timestep_limit = 200 # obs_space = sample_env.observation_space obs_space = spaces.Box(low=0, high=1, shape=(64 * 64 * 3,)) # action_space = sample_env.action_space action_space = spaces.Box(low=-1.0, high=1.0, shape=(args.action_size,)) print("Observation space:", obs_space) print("Action space:", action_space) # assert obs_space == spaces.Box(low=0, high=1, shape=(64 * 64 * 3,)) # assert action_space == spaces.Box(low=-1.0, high=1.0, shape=(args.action_size,)) # sample_env.close() assert isinstance(action_space, gym.spaces.Box) # Normalize observations based on their empirical mean and variance obs_normalizer = pfrl.nn.EmpiricalNormalization( obs_space.low.size, clip_threshold=5 ) obs_size = obs_space.low.size action_size = action_space.low.size policy = torch.nn.Sequential( nn.Linear(obs_size, 64), nn.Tanh(), nn.Linear(64, 64), nn.Tanh(), nn.Linear(64, action_size), pfrl.policies.GaussianHeadWithStateIndependentCovariance( action_size=action_size, var_type="diagonal", var_func=lambda x: torch.exp(2 * x), # Parameterize log std var_param_init=0, # log std = 0 => std = 1 ), ) vf = torch.nn.Sequential( nn.Linear(obs_size, 64), nn.Tanh(), nn.Linear(64, 64), nn.Tanh(), nn.Linear(64, 1), ) # While the original paper initialized weights by normal distribution, # we use orthogonal initialization as the latest openai/baselines does. def ortho_init(layer, gain): nn.init.orthogonal_(layer.weight, gain=gain) nn.init.zeros_(layer.bias) ortho_init(policy[0], gain=1) ortho_init(policy[2], gain=1) ortho_init(policy[4], gain=1e-2) ortho_init(vf[0], gain=1) ortho_init(vf[2], gain=1) ortho_init(vf[4], gain=1) # Combine a policy and a value function into a single model model = pfrl.nn.Branched(policy, vf) opt = torch.optim.Adam(model.parameters(), lr=3e-4, eps=1e-5) agent = PPO( model, opt, obs_normalizer=obs_normalizer, gpu=args.gpu, update_interval=args.update_interval, minibatch_size=args.batch_size, epochs=args.epochs, clip_eps_vf=None, entropy_coef=0, standardize_advantages=True, gamma=0.995, lambd=0.97, ) if args.load or args.load_pretrained: if args.load_pretrained: raise Exception("Pretrained models are currently unsupported.") # either load or load_pretrained must be false assert not args.load or not args.load_pretrained if args.load: agent.load(args.load) else: agent.load(utils.download_model("PPO", args.env, model_type="final")[0]) if args.demo: env = make_batch_env(True) eval_stats = experiments.eval_performance( env=env, agent=agent, n_steps=None, n_episodes=args.eval_n_runs, max_episode_len=timestep_limit, ) print( "n_runs: {} mean: {} median: {} stdev {}".format( args.eval_n_runs, eval_stats["mean"], eval_stats["median"], eval_stats["stdev"], ) ) else: experiments.train_agent_batch_with_evaluation( agent=agent, env=make_batch_env(False), eval_env=make_batch_env(True), outdir=args.outdir, steps=args.steps, eval_n_steps=None, eval_n_episodes=args.eval_n_runs, eval_interval=args.eval_interval, log_interval=args.log_interval, max_episode_len=timestep_limit, save_best_so_far_agent=True, )
def main(): parser = argparse.ArgumentParser() parser.add_argument( "--env", type=str, default="BreakoutNoFrameskip-v4", help="OpenAI Atari domain to perform algorithm on.", ) 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, help="GPU to use, set to -1 if no GPU.") parser.add_argument("--demo", action="store_true", default=False) parser.add_argument("--load-pretrained", action="store_true", default=False) parser.add_argument("--pretrained-type", type=str, default="best", choices=["best", "final"]) parser.add_argument("--load", type=str, default=None) parser.add_argument( "--log-level", type=int, default=20, help="Logging level. 10:DEBUG, 20:INFO etc.", ) parser.add_argument( "--render", action="store_true", default=False, help="Render env states in a GUI window.", ) parser.add_argument( "--monitor", action="store_true", default=False, help= ("Monitor env. Videos and additional information are saved as output files." ), ) parser.add_argument( "--steps", type=int, default=5 * 10**7, help="Total number of timesteps to train the agent.", ) parser.add_argument( "--replay-start-size", type=int, default=5 * 10**4, help="Minimum replay buffer size before " + "performing gradient updates.", ) parser.add_argument("--eval-n-steps", type=int, default=125000) parser.add_argument("--eval-interval", type=int, default=250000) parser.add_argument("--n-best-episodes", type=int, default=30) args = parser.parse_args() import logging logging.basicConfig(level=args.log_level) # Set a random seed used in PFRL. utils.set_random_seed(args.seed) # 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)) def make_env(test): # Use different random seeds for train and test envs env_seed = test_seed if test else train_seed env = atari_wrappers.wrap_deepmind( atari_wrappers.make_atari(args.env, max_frames=None), episode_life=not test, clip_rewards=not test, ) env.seed(int(env_seed)) if test: # Randomize actions like epsilon-greedy in evaluation as well env = pfrl.wrappers.RandomizeAction(env, 0.05) if args.monitor: env = pfrl.wrappers.Monitor( env, args.outdir, mode="evaluation" if test else "training") if args.render: env = pfrl.wrappers.Render(env) return env env = make_env(test=False) eval_env = make_env(test=True) n_actions = env.action_space.n q_func = nn.Sequential( pnn.LargeAtariCNN(), init_chainer_default(nn.Linear(512, n_actions)), DiscreteActionValueHead(), ) # Use the same hyperparameters as the Nature paper opt = pfrl.optimizers.RMSpropEpsInsideSqrt( q_func.parameters(), lr=2.5e-4, alpha=0.95, momentum=0.0, eps=1e-2, centered=True, ) rbuf = replay_buffers.ReplayBuffer(10**6) explorer = explorers.LinearDecayEpsilonGreedy( start_epsilon=1.0, end_epsilon=0.1, decay_steps=10**6, random_action_func=lambda: np.random.randint(n_actions), ) def phi(x): # Feature extractor return np.asarray(x, dtype=np.float32) / 255 Agent = agents.DQN agent = Agent( q_func, opt, rbuf, gpu=args.gpu, gamma=0.99, explorer=explorer, replay_start_size=args.replay_start_size, target_update_interval=10**4, clip_delta=True, update_interval=4, batch_accumulator="sum", phi=phi, ) if args.load or args.load_pretrained: # either load or load_pretrained must be false assert not args.load or not args.load_pretrained if args.load: agent.load(args.load) else: agent.load( utils.download_model("DQN", args.env, model_type=args.pretrained_type)[0]) if args.demo: eval_stats = experiments.eval_performance(env=eval_env, agent=agent, n_steps=args.eval_n_steps, n_episodes=None) print("n_episodes: {} mean: {} median: {} stdev {}".format( eval_stats["episodes"], eval_stats["mean"], eval_stats["median"], eval_stats["stdev"], )) else: experiments.train_agent_with_evaluation( agent=agent, env=env, steps=args.steps, eval_n_steps=args.eval_n_steps, eval_n_episodes=None, eval_interval=args.eval_interval, outdir=args.outdir, save_best_so_far_agent=True, eval_env=eval_env, ) dir_of_best_network = os.path.join(args.outdir, "best") agent.load(dir_of_best_network) # run 30 evaluation episodes, each capped at 5 mins of play stats = experiments.evaluator.eval_performance( env=eval_env, agent=agent, n_steps=None, n_episodes=args.n_best_episodes, max_episode_len=4500, logger=None, ) with open(os.path.join(args.outdir, "bestscores.json"), "w") as f: json.dump(stats, f) print("The results of the best scoring network:") for stat in stats: print(str(stat) + ":" + str(stats[stat]))
def main(): parser = argparse.ArgumentParser() parser.add_argument("--env", type=str, default="BreakoutNoFrameskip-v4") 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-pretrained", action="store_true", default=False) parser.add_argument("--pretrained-type", type=str, default="best", choices=["best", "final"]) parser.add_argument("--load", type=str, default=None) parser.add_argument("--final-exploration-frames", type=int, default=10**6) parser.add_argument("--final-epsilon", type=float, default=0.01) parser.add_argument("--eval-epsilon", type=float, default=0.001) parser.add_argument("--steps", type=int, default=5 * 10**7) parser.add_argument( "--max-frames", type=int, default=30 * 60 * 60, # 30 minutes with 60 fps help="Maximum number of frames for each episode.", ) 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=250000) parser.add_argument("--eval-n-steps", type=int, default=125000) parser.add_argument("--update-interval", type=int, default=4) parser.add_argument("--batch-size", type=int, default=32) parser.add_argument( "--log-level", type=int, default=20, help="Logging level. 10:DEBUG, 20:INFO etc.", ) parser.add_argument( "--render", action="store_true", default=False, help="Render env states in a GUI window.", ) parser.add_argument( "--monitor", action="store_true", default=False, help= ("Monitor env. Videos and additional information are saved as output files." ), ) parser.add_argument("--batch-accumulator", type=str, default="mean", choices=["mean", "sum"]) parser.add_argument("--quantile-thresholds-N", type=int, default=64) parser.add_argument("--quantile-thresholds-N-prime", type=int, default=64) parser.add_argument("--quantile-thresholds-K", type=int, default=32) parser.add_argument("--n-best-episodes", type=int, default=200) args = parser.parse_args() import logging logging.basicConfig(level=args.log_level) # Set a random seed used in PFRL. utils.set_random_seed(args.seed) # 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)) def make_env(test): # Use different random seeds for train and test envs env_seed = test_seed if test else train_seed env = atari_wrappers.wrap_deepmind( atari_wrappers.make_atari(args.env, max_frames=args.max_frames), episode_life=not test, clip_rewards=not test, ) env.seed(int(env_seed)) if test: # Randomize actions like epsilon-greedy in evaluation as well env = pfrl.wrappers.RandomizeAction(env, args.eval_epsilon) if args.monitor: env = pfrl.wrappers.Monitor( env, args.outdir, mode="evaluation" if test else "training") if args.render: env = pfrl.wrappers.Render(env) return env env = make_env(test=False) eval_env = make_env(test=True) n_actions = env.action_space.n q_func = pfrl.agents.iqn.ImplicitQuantileQFunction( psi=nn.Sequential( nn.Conv2d(4, 32, 8, stride=4), nn.ReLU(), nn.Conv2d(32, 64, 4, stride=2), nn.ReLU(), nn.Conv2d(64, 64, 3, stride=1), nn.ReLU(), nn.Flatten(), ), phi=nn.Sequential( pfrl.agents.iqn.CosineBasisLinear(64, 3136), nn.ReLU(), ), f=nn.Sequential( nn.Linear(3136, 512), nn.ReLU(), nn.Linear(512, n_actions), ), ) # Use the same hyper parameters as https://arxiv.org/abs/1710.10044 opt = torch.optim.Adam(q_func.parameters(), lr=5e-5, eps=1e-2 / args.batch_size) rbuf = replay_buffers.ReplayBuffer(10**6) explorer = explorers.LinearDecayEpsilonGreedy( 1.0, args.final_epsilon, args.final_exploration_frames, lambda: np.random.randint(n_actions), ) def phi(x): # Feature extractor return np.asarray(x, dtype=np.float32) / 255 agent = pfrl.agents.IQN( 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=args.batch_accumulator, phi=phi, quantile_thresholds_N=args.quantile_thresholds_N, quantile_thresholds_N_prime=args.quantile_thresholds_N_prime, quantile_thresholds_K=args.quantile_thresholds_K, ) if args.load or args.load_pretrained: # either load or load_pretrained must be false assert not args.load or not args.load_pretrained if args.load: agent.load(args.load) else: agent.load( utils.download_model("IQN", args.env, model_type=args.pretrained_type)[0]) if args.demo: eval_stats = experiments.eval_performance( env=eval_env, agent=agent, n_steps=args.eval_n_steps, n_episodes=None, ) print("n_steps: {} mean: {} median: {} stdev {}".format( args.eval_n_steps, eval_stats["mean"], eval_stats["median"], eval_stats["stdev"], )) else: experiments.train_agent_with_evaluation( agent=agent, env=env, steps=args.steps, eval_n_steps=args.eval_n_steps, eval_n_episodes=None, eval_interval=args.eval_interval, outdir=args.outdir, save_best_so_far_agent=True, eval_env=eval_env, ) dir_of_best_network = os.path.join(args.outdir, "best") agent.load(dir_of_best_network) # run 200 evaluation episodes, each capped at 30 mins of play stats = experiments.evaluator.eval_performance( env=eval_env, agent=agent, n_steps=None, n_episodes=args.n_best_episodes, max_episode_len=args.max_frames / 4, logger=None, ) with open(os.path.join(args.outdir, "bestscores.json"), "w") as f: json.dump(stats, f) print("The results of the best scoring network:") for stat in stats: print(str(stat) + ":" + str(stats[stat]))
def main(): parser = argparse.ArgumentParser() parser.add_argument("--env", type=str, default="BreakoutNoFrameskip-v4") 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-pretrained", action="store_true", default=False) parser.add_argument("--pretrained-type", type=str, default="best", choices=["best", "final"]) parser.add_argument("--load", type=str, default=None) parser.add_argument("--eval-epsilon", type=float, default=0.0) parser.add_argument("--noisy-net-sigma", type=float, default=0.5) parser.add_argument("--steps", type=int, default=5 * 10**7) parser.add_argument( "--max-frames", type=int, default=30 * 60 * 60, # 30 minutes with 60 fps help="Maximum number of frames for each episode.", ) parser.add_argument("--replay-start-size", type=int, default=2 * 10**4) parser.add_argument("--eval-n-steps", type=int, default=125000) parser.add_argument("--eval-interval", type=int, default=250000) parser.add_argument( "--log-level", type=int, default=20, help="Logging level. 10:DEBUG, 20:INFO etc.", ) parser.add_argument( "--render", action="store_true", default=False, help="Render env states in a GUI window.", ) parser.add_argument( "--monitor", action="store_true", default=False, help= ("Monitor env. Videos and additional information are saved as output files." ), ) parser.add_argument("--n-best-episodes", type=int, default=200) args = parser.parse_args() import logging logging.basicConfig(level=args.log_level) # Set a random seed used in PFRL. utils.set_random_seed(args.seed) # 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)) def make_env(test): # Use different random seeds for train and test envs env_seed = test_seed if test else train_seed env = atari_wrappers.wrap_deepmind( atari_wrappers.make_atari(args.env, max_frames=args.max_frames), episode_life=not test, clip_rewards=not test, ) env.seed(int(env_seed)) if test: # Randomize actions like epsilon-greedy in evaluation as well env = pfrl.wrappers.RandomizeAction(env, args.eval_epsilon) if args.monitor: env = pfrl.wrappers.Monitor( env, args.outdir, mode="evaluation" if test else "training") if args.render: env = pfrl.wrappers.Render(env) return env env = make_env(test=False) eval_env = make_env(test=True) n_actions = env.action_space.n n_atoms = 51 v_max = 10 v_min = -10 q_func = DistributionalDuelingDQN( n_actions, n_atoms, v_min, v_max, ) # Noisy nets pnn.to_factorized_noisy(q_func, sigma_scale=args.noisy_net_sigma) # Turn off explorer explorer = explorers.Greedy() # Use the same hyper parameters as https://arxiv.org/abs/1710.02298 opt = torch.optim.Adam(q_func.parameters(), 6.25e-5, eps=1.5 * 10**-4) # Prioritized Replay # Anneal beta from beta0 to 1 throughout training update_interval = 4 betasteps = args.steps / update_interval rbuf = replay_buffers.PrioritizedReplayBuffer( 10**6, alpha=0.5, beta0=0.4, betasteps=betasteps, num_steps=3, normalize_by_max="memory", ) def phi(x): # Feature extractor return np.asarray(x, dtype=np.float32) / 255 Agent = agents.CategoricalDoubleDQN agent = Agent( q_func, opt, rbuf, gpu=args.gpu, gamma=0.99, explorer=explorer, minibatch_size=32, replay_start_size=args.replay_start_size, target_update_interval=32000, update_interval=update_interval, batch_accumulator="mean", phi=phi, ) if args.load or args.load_pretrained: # either load_ or load_pretrained must be false assert not args.load or not args.load_pretrained if args.load: agent.load(args.load) else: agent.load( utils.download_model("Rainbow", args.env, model_type=args.pretrained_type)[0]) if args.demo: eval_stats = experiments.eval_performance(env=eval_env, agent=agent, n_steps=args.eval_n_steps, n_episodes=None) print("n_episodes: {} mean: {} median: {} stdev {}".format( eval_stats["episodes"], eval_stats["mean"], eval_stats["median"], eval_stats["stdev"], )) else: experiments.train_agent_with_evaluation( agent=agent, env=env, steps=args.steps, eval_n_steps=args.eval_n_steps, eval_n_episodes=None, eval_interval=args.eval_interval, outdir=args.outdir, save_best_so_far_agent=True, eval_env=eval_env, ) dir_of_best_network = os.path.join(args.outdir, "best") agent.load(dir_of_best_network) # run 200 evaluation episodes, each capped at 30 mins of play stats = experiments.evaluator.eval_performance( env=eval_env, agent=agent, n_steps=None, n_episodes=args.n_best_episodes, max_episode_len=args.max_frames / 4, logger=None, ) with open(os.path.join(args.outdir, "bestscores.json"), "w") as f: json.dump(stats, f) print("The results of the best scoring network:") for stat in stats: print(str(stat) + ":" + str(stats[stat]))
def main(): parser = argparse.ArgumentParser() parser.add_argument("--processes", type=int, default=16) parser.add_argument("--env", type=str, default="BreakoutNoFrameskip-v4") 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("--t-max", type=int, default=5) 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( "--max-frames", type=int, default=30 * 60 * 60, # 30 minutes with 60 fps help="Maximum number of frames for each episode.", ) parser.add_argument("--lr", type=float, default=7e-4) parser.add_argument("--eval-interval", type=int, default=250000) parser.add_argument("--eval-n-steps", type=int, default=125000) parser.add_argument("--demo", action="store_true", default=False) parser.add_argument("--load-pretrained", action="store_true", default=False) parser.add_argument("--pretrained-type", type=str, default="best", choices=["best", "final"]) parser.add_argument("--load", type=str, default="") parser.add_argument( "--log-level", type=int, default=20, help="Logging level. 10:DEBUG, 20:INFO etc.", ) parser.add_argument( "--render", action="store_true", default=False, help="Render env states in a GUI window.", ) parser.add_argument( "--monitor", action="store_true", default=False, help= ("Monitor env. Videos and additional information are saved as output files." ), ) args = parser.parse_args() import logging logging.basicConfig(level=args.log_level) # Set a random seed used in PFRL. # If you use more than one processes, the results will be no longer # deterministic even with the same random seed. utils.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)) 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 = atari_wrappers.wrap_deepmind( atari_wrappers.make_atari(args.env, max_frames=args.max_frames), episode_life=not test, clip_rewards=not test, ) env.seed(int(env_seed)) if args.monitor: env = pfrl.wrappers.Monitor( env, args.outdir, mode="evaluation" if test else "training") if args.render: env = pfrl.wrappers.Render(env) return env sample_env = make_env(0, False) obs_size = sample_env.observation_space.low.shape[0] n_actions = sample_env.action_space.n model = nn.Sequential( nn.Conv2d(obs_size, 16, 8, stride=4), nn.ReLU(), nn.Conv2d(16, 32, 4, stride=2), nn.ReLU(), nn.Flatten(), nn.Linear(2592, 256), nn.ReLU(), pfrl.nn.Branched( nn.Sequential( nn.Linear(256, n_actions), SoftmaxCategoricalHead(), ), nn.Linear(256, 1), ), ) # SharedRMSprop is same as torch.optim.RMSprop except that it initializes # its state in __init__, allowing it to be moved to shared memory. opt = SharedRMSpropEpsInsideSqrt(model.parameters(), lr=7e-4, eps=1e-1, alpha=0.99) assert opt.state_dict()["state"], ( "To share optimizer state across processes, the state must be" " initialized before training.") def phi(x): # Feature extractor return np.asarray(x, dtype=np.float32) / 255 agent = a3c.A3C( model, opt, t_max=args.t_max, gamma=0.99, beta=args.beta, phi=phi, max_grad_norm=40.0, ) if args.load or args.load_pretrained: # either load or load_pretrained must be false assert not args.load or not args.load_pretrained if args.load: agent.load(args.load) else: agent.load( utils.download_model("A3C", args.env, model_type=args.pretrained_type)[0]) if args.demo: env = make_env(0, True) eval_stats = experiments.eval_performance(env=env, agent=agent, n_steps=args.eval_n_steps, n_episodes=None) print("n_steps: {} mean: {} median: {} stdev: {}".format( args.eval_n_steps, eval_stats["mean"], eval_stats["median"], eval_stats["stdev"], )) else: # Linearly decay the learning rate to zero def lr_setter(env, agent, value): for pg in agent.optimizer.param_groups: assert "lr" in pg pg["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_steps=args.eval_n_steps, eval_n_episodes=None, eval_interval=args.eval_interval, global_step_hooks=[lr_decay_hook], save_best_so_far_agent=True, )