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 make_model(self, env): obs_size = env.observation_space.low.size action_size = env.action_space.low.size hidden_size = 50 policy = nn.Sequential( nn.Linear(obs_size, hidden_size), nn.ReLU(), nn.Linear(hidden_size, action_size), BoundByTanh(low=env.action_space.low, high=env.action_space.high), DeterministicHead(), ) q_func = nn.Sequential( ConcatObsAndAction(), nn.Linear(obs_size + action_size, hidden_size), nn.ReLU(), nn.Linear(hidden_size, 1), ) return policy, q_func
nn.ReLU(), nn.Linear(512, 512), nn.ReLU(), nn.Linear(512, 512), nn.ReLU(), nn.Linear(512, 1), ) policy = nn.Sequential( nn.Linear(obs_size, 512), nn.ReLU(), nn.Linear(512, 512), nn.ReLU(), nn.Linear(512, 512), nn.ReLU(), nn.Linear(512, action_size), BoundByTanh(low=action_space.low, high=action_space.high), DeterministicHead(), ) model = nn.ModuleList([policy, q_func]) model.load_state_dict(torch.load("./results/best/model.pt")) policy = model[0] print("finish loading") with torch.no_grad(): for i_episode in range(20): observation = env.reset() for t in range(100): env.render() observation = torch.tensor(observation).float() action = policy(observation).rsample()
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 q_func = nn.Sequential( ConcatObsAndAction(), nn.Linear(obs_size + action_size, 400), nn.ReLU(), nn.Linear(400, 300), nn.ReLU(), nn.Linear(300, 1), ) policy = nn.Sequential( nn.Linear(obs_size, 400), nn.ReLU(), nn.Linear(400, 300), nn.ReLU(), nn.Linear(300, action_size), BoundByTanh(low=action_space.low, high=action_space.high), DeterministicHead(), ) opt_a = torch.optim.Adam(policy.parameters()) opt_c = torch.optim.Adam(q_func.parameters()) 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 = DDPG( policy, q_func, opt_a, opt_c, rbuf, gamma=0.99, explorer=explorer, replay_start_size=args.replay_start_size, target_update_method="soft", target_update_interval=1, update_interval=1, soft_update_tau=5e-3, n_times_update=1, gpu=args.gpu, minibatch_size=args.batch_size, burnin_action_func=burnin_action_func, ) 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("DDPG", 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"], )) 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="'DClawTurnFixed-v0'", 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=-1, 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( "--max-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=64, 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." ) parser.add_argument("--gamma", type=float, default=0.9) parser.add_argument("--ddpg-training-steps", type=int, default=int(1e3)) parser.add_argument("--adversary-training-steps", type=int,default=int(1e3)) args = parser.parse_args() logging.basicConfig(level=args.log_level) args.outdir = './results' 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('DClawTurnFixed-v0') # 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 q_func = nn.Sequential( ConcatObsAndAction(), nn.Linear(obs_size + action_size, 256), nn.ReLU(), nn.Linear(256, 256), nn.ReLU(), nn.Linear(256,256), nn.ReLU(), nn.Linear(256, 1), ) policy = nn.Sequential( nn.Linear(obs_size, 256), nn.ReLU(), nn.Linear(256, 256), nn.ReLU(), nn.Linear(256,256), nn.ReLU(), nn.Linear(256, action_size), BoundByTanh(low=action_space.low, high=action_space.high), DeterministicHead(), ) ddpg_opt_a = torch.optim.Adam(policy.parameters()) ddpg_opt_c = torch.optim.Adam(q_func.parameters()) ddpg_rbuf = replay_buffers.ReplayBuffer(10 ** 6) ddpg_explorer = explorers.AdditiveGaussian( scale=0.1, low=action_space.low, high=action_space.high ) def ddpg_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 ddpg_agent = DDPG( policy, q_func, ddpg_opt_a, ddpg_opt_c, ddpg_rbuf, gamma=args.gamma, explorer=ddpg_explorer, replay_start_size=args.replay_start_size, target_update_method="soft", target_update_interval=1, update_interval=1, soft_update_tau=5e-3, n_times_update=1, gpu=args.gpu, minibatch_size=args.batch_size, burnin_action_func=ddpg_burnin_action_func, ) def adversary_random_func(): return np.random.randint(0,9) # adversary_q = Critic(obs_size, 1, hidden_size=adversary_hidden_size) # adversary_action_space = gym.spaces.discrete.Discrete(9) # adversary_q = q_functions.FCQuadraticStateQFunction( # obs_size, 1, n_hidden_channels = 256, n_hidden_layers = 2,action_space = adversary_action_space # ) adversary_q = nn.Sequential( nn.Linear(obs_size, 256), nn.Linear(256,256), nn.Linear(256,256), nn.Linear(256,1), DiscreteActionValueHead(), ) adversary_optimizer = torch.optim.Adam(adversary_q.parameters(), lr=1e-3) adversary_rbuf_capacity = int(1e6) adversary_rbuf = replay_buffers.ReplayBuffer(adversary_rbuf_capacity) adversary_explorer = explorers.LinearDecayEpsilonGreedy( 1.0, 0.1, 10**4, adversary_random_func ) adversary_agent = DQN( adversary_q, adversary_optimizer, adversary_rbuf, gpu=args.gpu, gamma=args.gamma, explorer=adversary_explorer, replay_start_size=args.replay_start_size, target_update_interval=1, minibatch_size=args.batch_size, target_update_method='soft', soft_update_tau=5e-3 ) logger = logging.getLogger(__name__) eval_env = make_env(test=True) evaluator = Evaluator( agent=ddpg_agent, n_steps=None, n_episodes=args.eval_n_runs, eval_interval=args.eval_interval, outdir=args.outdir, max_episode_len=timestep_limit, env=eval_env, step_offset=0, save_best_so_far_agent=True, use_tensorboard=True, logger=logger, ) episode_reward = 0 ddpg_episode_idx = 0 adversary_episode_idx = 0 # o_0, r_0 current_state = env.reset() t = 0 ddpg_t = 0 adversary_t = 0 episode_len = 0 try: while t < args.max_steps: for i in range(args.ddpg_training_steps): t += 1 ddpg_t += 1 ddpg_action = ddpg_agent.act(current_state) adversary_action = adversary_agent.act(current_state) ddpg_action[adversary_action] = 0 next_state, reward, done, info = env.step(ddpg_action) episode_reward += reward episode_len += 1 reset = episode_len == timestep_limit or info.get("needs_reset", False) ddpg_agent.observe(next_state, reward, done, reset) current_state = next_state if done or reset or t == args.max_steps: logger.info( "ddpg phase: outdir:%s step:%s episode:%s R:%s", args.outdir, ddpg_t, ddpg_episode_idx, episode_reward, ) logger.info("statistics:%s", ddpg_agent.get_statistics()) if evaluator is not None: evaluator.evaluate_if_necessary(t=t, episodes=ddpg_episode_idx + 1) if t == args.max_steps: break episode_reward = 0 ddpg_episode_idx += 1 episode_len = 0 current_state = env.reset() episode_reward = 0 episode_len = 0 current_state = env.reset() print("start adversary training ") for i in range(args.adversary_training_steps): t += 1 adversary_t += 1 ddpg_action = ddpg_agent.act(current_state) adversary_action = adversary_agent.act(current_state) ddpg_action[adversary_action] = 0 next_state, reward, done, info = env.step(ddpg_action) reward = -reward episode_len += 1 reset = episode_len == timestep_limit or info.get("needs_reset", False) adversary_agent.observe(next_state, reward, done, reset) current_state = next_state if done or reset or t == args.max_steps: if t == args.max_steps: break episode_reward = 0 adversary_episode_idx += 1 episode_len = 0 current_state = env.reset() except (Exception, KeyboardInterrupt): # Save the current model before being killed save_agent(ddpg_agent, t, args.outdir, logger, suffix="_ddpg_except") save_agent(adversary_agent, t, args.outdir, logger, suffix="_adversary_except" ) raise # Save the final model save_agent(ddpg_agent, t, args.outdir, logger, suffix="_ddpg_finish") save_agent(adversary_agent, t, args.outdir, logger, suffix="_adversary_finish" ) # if args.demo: # eval_env.render() # eval_stats = experiments.eval_performance( # env=eval_env, # agent=ddpg_agent, # n_steps=None, # n_episodes=args.eval_n_runbase_envs, # 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_with_evaluation( # agent=ddpg_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, # ) print("finish")