def test_learning_rnn(self): pol_net = PolNetLSTM( self.env.observation_space, self.env.action_space, h_size=32, cell_size=32) pol = CategoricalPol( self.env.observation_space, self.env.action_space, pol_net, rnn=True) vf_net = VNetLSTM(self.env.observation_space, h_size=32, cell_size=32) vf = DeterministicSVfunc(self.env.observation_space, vf_net, rnn=True) sampler = EpiSampler(self.env, pol, num_parallel=1) optim_vf = torch.optim.Adam(vf_net.parameters(), 3e-4) epis = sampler.sample(pol, max_steps=400) traj = Traj() traj.add_epis(epis) traj = ef.compute_vs(traj, vf) traj = ef.compute_rets(traj, 0.99) traj = ef.compute_advs(traj, 0.99, 0.95) traj = ef.centerize_advs(traj) traj = ef.compute_h_masks(traj) traj.register_epis() result_dict = trpo.train(traj, pol, vf, optim_vf, 1, 2) del sampler
def test_learning(self): pol_net = PolNet(self.env.observation_space, self.env.action_space, h1=32, h2=32) pol = CategoricalPol(self.env.observation_space, self.env.action_space, pol_net) vf_net = VNet(self.env.observation_space, h1=32, h2=32) vf = DeterministicSVfunc(self.env.observation_space, vf_net) sampler = EpiSampler(self.env, pol, num_parallel=1) optim_pol = torch.optim.Adam(pol_net.parameters(), 3e-4) optim_vf = torch.optim.Adam(vf_net.parameters(), 3e-4) epis = sampler.sample(pol, max_steps=32) traj = Traj() traj.add_epis(epis) traj = ef.compute_vs(traj, vf) traj = ef.compute_rets(traj, 0.99) traj = ef.compute_advs(traj, 0.99, 0.95) traj = ef.centerize_advs(traj) traj = ef.compute_h_masks(traj) traj.register_epis() result_dict = ppo_clip.train(traj=traj, pol=pol, vf=vf, clip_param=0.2, optim_pol=optim_pol, optim_vf=optim_vf, epoch=1, batch_size=32) result_dict = ppo_kl.train(traj=traj, pol=pol, vf=vf, kl_beta=0.1, kl_targ=0.2, optim_pol=optim_pol, optim_vf=optim_vf, epoch=1, batch_size=32, max_grad_norm=10) del sampler
env.env.seed(args.seed) if args.c2d: env = C2DEnv(env) observation_space = env.observation_space action_space = env.action_space pol_net = PolNet(observation_space, action_space) if isinstance(action_space, gym.spaces.Box): pol = GaussianPol(observation_space, action_space, pol_net, data_parallel=args.data_parallel) elif isinstance(action_space, gym.spaces.Discrete): pol = CategoricalPol(observation_space, action_space, pol_net, data_parallel=args.data_parallel) elif isinstance(action_space, gym.spaces.MultiDiscrete): pol = MultiCategoricalPol(observation_space, action_space, pol_net, data_parallel=args.data_parallel) else: raise ValueError('Only Box, Discrete, and MultiDiscrete are supported') vf_net = VNet(observation_space) vf = DeterministicSVfunc(observation_space, vf_net, data_parallel=args.data_parallel) sampler = EpiSampler(env, pol, num_parallel=args.num_parallel, seed=args.seed)
# Generate teacher (t) policy and student (s) policy and load teacher policy # Please note that the two policies do not have to have the same hidden architecture if args.rnn: t_pol_net = PolNetLSTM(observation_space, action_space, h_size=256, cell_size=256) s_pol_net = PolNetLSTM(observation_space, action_space, h_size=256, cell_size=256) else: t_pol_net = PolNet(observation_space, action_space) s_pol_net = PolNet(observation_space, action_space, h1=190, h2=90) if isinstance(action_space, gym.spaces.Box): t_pol = GaussianPol(observation_space, action_space, t_pol_net, args.rnn) s_pol = GaussianPol(observation_space, action_space, s_pol_net, args.rnn) elif isinstance(action_space, gym.spaces.Discrete): t_pol = CategoricalPol( observation_space, action_space, t_pol_net, args.rnn) s_pol = CategoricalPol( observation_space, action_space, s_pol_net, args.rnn) elif isinstance(action_space, gym.spaces.MultiDiscrete): t_pol = MultiCategoricalPol( observation_space, action_space, t_pol_net, args.rnn) s_pol = MultiCategoricalPol( observation_space, action_space, s_pol_net, args.rnn) else: raise ValueError('Only Box, Discrete and Multidiscrete are supported') if args.teacher_pol: t_pol.load_state_dict(torch.load( os.path.join(args.teacher_dir, args.teacher_fname))) if args.rnn:
ac_space = env.action_space # Generate teacher (t) policy and student (s) policy and load teacher policy # Please note that the two policies do not have to have the same hidden architecture if args.rnn: t_pol_net = PolNetLSTM(ob_space, ac_space, h_size=256, cell_size=256) s_pol_net = PolNetLSTM(ob_space, ac_space, h_size=256, cell_size=256) else: t_pol_net = PolNet(ob_space, ac_space) s_pol_net = PolNet(ob_space, ac_space, h1=190, h2=90) if isinstance(ac_space, gym.spaces.Box): t_pol = GaussianPol(ob_space, ac_space, t_pol_net, args.rnn) s_pol = GaussianPol(ob_space, ac_space, s_pol_net, args.rnn) elif isinstance(ac_space, gym.spaces.Discrete): t_pol = CategoricalPol(ob_space, ac_space, t_pol_net, args.rnn) s_pol = CategoricalPol(ob_space, ac_space, s_pol_net, args.rnn) elif isinstance(ac_space, gym.spaces.MultiDiscrete): t_pol = MultiCategoricalPol(ob_space, ac_space, t_pol_net, args.rnn) s_pol = MultiCategoricalPol(ob_space, ac_space, s_pol_net, args.rnn) else: raise ValueError('Only Box, Discrete and Multidiscrete are supported') if args.teacher_pol: t_pol.load_state_dict( torch.load(os.path.join(args.teacher_dir, args.teacher_fname))) if args.rnn: s_vf_net = VNetLSTM(ob_space, h_size=256, cell_size=256) else: s_vf_net = VNet(ob_space)
if args.rnn: pol_net = PolNetLSTM(ob_space, ac_space, h_size=256, cell_size=256) else: pol_net = PolNet(ob_space, ac_space) if isinstance(ac_space, gym.spaces.Box): pol = GaussianPol(ob_space, ac_space, pol_net, args.rnn, data_parallel=args.data_parallel, parallel_dim=1 if args.rnn else 0) elif isinstance(ac_space, gym.spaces.Discrete): pol = CategoricalPol(ob_space, ac_space, pol_net, args.rnn, data_parallel=args.data_parallel, parallel_dim=1 if args.rnn else 0) elif isinstance(ac_space, gym.spaces.MultiDiscrete): pol = MultiCategoricalPol(ob_space, ac_space, pol_net, args.rnn, data_parallel=args.data_parallel, parallel_dim=1 if args.rnn else 0) else: raise ValueError('Only Box, Discrete, and MultiDiscrete are supported') if args.rnn: vf_net = VNetLSTM(ob_space, h_size=256, cell_size=256) else:
def main(args): init_ray(args.num_cpus, args.num_gpus, args.ray_redis_address) if not os.path.exists(args.log): os.makedirs(args.log) if not os.path.exists(os.path.join(args.log, 'models')): os.mkdir(os.path.join(args.log, 'models')) score_file = os.path.join(args.log, 'progress.csv') logger.add_tabular_output(score_file) logger.add_tensorboard_output(args.log) with open(os.path.join(args.log, 'args.json'), 'w') as f: json.dump(vars(args), f) pprint(vars(args)) # when doing the distributed training, disable video recordings env = GymEnv(args.env_name) env.env.seed(args.seed) if args.c2d: env = C2DEnv(env) observation_space = env.observation_space action_space = env.action_space pol_net = PolNet(observation_space, action_space) rnn = False # pol_net = PolNetLSTM(observation_space, action_space) # rnn = True if isinstance(action_space, gym.spaces.Box): pol = GaussianPol(observation_space, action_space, pol_net, rnn=rnn) elif isinstance(action_space, gym.spaces.Discrete): pol = CategoricalPol(observation_space, action_space, pol_net) elif isinstance(action_space, gym.spaces.MultiDiscrete): pol = MultiCategoricalPol(observation_space, action_space, pol_net) else: raise ValueError('Only Box, Discrete, and MultiDiscrete are supported') vf_net = VNet(observation_space) vf = DeterministicSVfunc(observation_space, vf_net) trainer = TrainManager(Trainer, args.num_trainer, args.master_address, args=args, vf=vf, pol=pol) sampler = EpiSampler(env, pol, args.num_parallel, seed=args.seed) total_epi = 0 total_step = 0 max_rew = -1e6 start_time = time.time() while args.max_epis > total_epi: with measure('sample'): sampler.set_pol_state(trainer.get_state("pol")) epis = sampler.sample(max_steps=args.max_steps_per_iter) with measure('train'): result_dict = trainer.train(epis=epis) step = result_dict["traj_num_step"] total_step += step total_epi += result_dict["traj_num_epi"] rewards = [np.sum(epi['rews']) for epi in epis] mean_rew = np.mean(rewards) elapsed_time = time.time() - start_time logger.record_tabular('ElapsedTime', elapsed_time) logger.record_results(args.log, result_dict, score_file, total_epi, step, total_step, rewards, plot_title=args.env_name) with measure('save'): pol_state = trainer.get_state("pol") vf_state = trainer.get_state("vf") optim_pol_state = trainer.get_state("optim_pol") optim_vf_state = trainer.get_state("optim_vf") torch.save(pol_state, os.path.join(args.log, 'models', 'pol_last.pkl')) torch.save(vf_state, os.path.join(args.log, 'models', 'vf_last.pkl')) torch.save(optim_pol_state, os.path.join(args.log, 'models', 'optim_pol_last.pkl')) torch.save(optim_vf_state, os.path.join(args.log, 'models', 'optim_vf_last.pkl')) if mean_rew > max_rew: torch.save(pol_state, os.path.join(args.log, 'models', 'pol_max.pkl')) torch.save(vf_state, os.path.join(args.log, 'models', 'vf_max.pkl')) torch.save( optim_pol_state, os.path.join(args.log, 'models', 'optim_pol_max.pkl')) torch.save( optim_vf_state, os.path.join(args.log, 'models', 'optim_vf_max.pkl')) max_rew = mean_rew del sampler del trainer
env = GymEnv(args.env_name, log_dir=os.path.join(args.log, 'movie'), record_video=args.record) env.env.seed(args.seed) if args.c2d: env = C2DEnv(env) observation_space = env.observation_space action_space = env.action_space pol_net = PolNet(observation_space, action_space) if isinstance(action_space, gym.spaces.Box): pol = GaussianPol(observation_space, action_space, pol_net) elif isinstance(action_space, gym.spaces.Discrete): pol = CategoricalPol(observation_space, action_space, pol_net) elif isinstance(action_space, gym.spaces.MultiDiscrete): pol = MultiCategoricalPol(observation_space, action_space, pol_net) else: raise ValueError('Only Box, Discrete, and MultiDiscrete are supported') sampler = EpiSampler(env, pol, num_parallel=args.num_parallel, seed=args.seed) optim_pol = torch.optim.Adam(pol_net.parameters(), args.pol_lr) with open(os.path.join(args.expert_dir, args.expert_fname), 'rb') as f: expert_epis = pickle.load(f) train_epis, test_epis = ef.train_test_split(expert_epis, train_size=args.train_size) train_traj = Traj() train_traj.add_epis(train_epis) train_traj.register_epis()
env = GymEnv(args.env_name, log_dir=os.path.join( args.log, 'movie'), record_video=args.record) env.env.seed(args.seed) if args.c2d: env = C2DEnv(env) ob_space = env.observation_space ac_space = env.action_space pol_net = PolNet(ob_space, ac_space) if isinstance(ac_space, gym.spaces.Box): pol = GaussianPol(ob_space, ac_space, pol_net, data_parallel=args.data_parallel) elif isinstance(ac_space, gym.spaces.Discrete): pol = CategoricalPol(ob_space, ac_space, pol_net, data_parallel=args.data_parallel) elif isinstance(ac_space, gym.spaces.MultiDiscrete): pol = MultiCategoricalPol( ob_space, ac_space, pol_net, data_parallel=args.data_parallel) else: raise ValueError('Only Box, Discrete, and MultiDiscrete are supported') vf_net = VNet(ob_space) vf = DeterministicSVfunc(ob_space, vf_net, data_parallel=args.data_parallel) if args.rew_type == 'rew': rewf_net = VNet(ob_space, h1=args.discrim_h1, h2=args.discrim_h2) rewf = DeterministicSVfunc( ob_space, rewf_net, data_parallel=args.data_parallel) shaping_vf_net = VNet(ob_space, h1=args.discrim_h1, h2=args.discrim_h2)
def main(): pygame.init() # 初期化 (w, h) = (480, 320) screen = pygame.display.set_mode((w, h), FULLSCREEN) # window size pygame.display.set_caption("Sikamaru") # window bar # initialization tx = 0 ty = 0 sika = Sikamaru((w / 2, h / 2)) sleep_count = 5 eat_mode = 100 esa = Food() wait = True seed = 42 # TODO define RL agent ''' state : 4D (sikaposi, esaposi) action : 2D (-20,+20)^2 SAC simple_net : 30,30 ''' np.random.seed(seed) torch.manual_seed(seed) low = np.zeros(4) high = w * np.ones(4) ob_space = gym.spaces.Box(low=low, high=high) ac_space = gym.spaces.Discrete(4) ac_dict = { 0: np.array([-20, 0]), 1: np.array([20, 0]), 2: np.array([0, -20]), 3: np.array([0, 20]) } pol_net = PolNet(ob_space, ac_space) pol = CategoricalPol(ob_space, ac_space, pol_net) qf_net1 = QNet(ob_space, ac_space) qf1 = DeterministicSAVfunc(ob_space, ac_space, qf_net1) targ_qf_net1 = QNet(ob_space, ac_space) targ_qf_net1.load_state_dict(qf_net1.state_dict()) targ_qf1 = DeterministicSAVfunc(ob_space, ac_space, targ_qf_net1) qf_net2 = QNet(ob_space, ac_space) qf2 = DeterministicSAVfunc(ob_space, ac_space, qf_net2) targ_qf_net2 = QNet(ob_space, ac_space) targ_qf_net2.load_state_dict(qf_net2.state_dict()) targ_qf2 = DeterministicSAVfunc(ob_space, ac_space, targ_qf_net2) qfs = [qf1, qf2] targ_qfs = [targ_qf1, targ_qf2] log_alpha = nn.Parameter(torch.ones(())) optim_pol = torch.optim.Adam(pol_net.parameters(), 1e-4) optim_qf1 = torch.optim.Adam(qf_net1.parameters(), 3e-4) optim_qf2 = torch.optim.Adam(qf_net2.parameters(), 3e-4) optim_qfs = [optim_qf1, optim_qf2] optim_alpha = torch.optim.Adam([log_alpha], 1e-4) # off_traj = Traj() while (True): screen.fill(( 0, 100, 0, )) # backgroud color # my procedure ## env obs = make_obs((tx, ty), sika.posi, w, h) ac_real, ac, a_i = pol.deterministic_ac_real( torch.tensor(obs, dtype=torch.float)) # ac_real = ac_real.reshape(pol.ac_space.shape) a = rule_act((tx, ty), sika.posi) # a = ac_dict[int(ac_real)] nx = sika.posi[0] + a[0] nx = max(min(nx, w), 0) ny = sika.posi[1] + a[1] ny = max(min(ny, h), 0) sika.move((nx, ny)) screen.blit(sika.get_im(), sika.rect) if esa.life: # RL # TOOD:record as epi screen.blit(esa.im, esa.rect) # scr rew = esa.life_step(sika) if rew > 0: sika.bigup() if esa.life == 0: pass #TODO add one epi and learn wait = False if wait: pygame.time.wait(500) wait = True pygame.display.update() # 画面更新 ## event for event in pygame.event.get(): if event.type == MOUSEBUTTONDOWN and event.button == 1: tx, ty = event.pos esa.set((tx, ty)) if event.type == KEYDOWN: if event.key == K_ESCAPE: sys.exit() if event.type == QUIT: # 終了処理 pygame.quit() sys.exit()
action_space, h_size=256, cell_size=256) else: pol_net = PolNet(observation_space, action_space) if isinstance(action_space, gym.spaces.Box): pol = GaussianPol(observation_space, action_space, pol_net, args.rnn, data_parallel=args.ddp, parallel_dim=1 if args.rnn else 0) elif isinstance(action_space, gym.spaces.Discrete): pol = CategoricalPol(observation_space, action_space, pol_net, args.rnn, data_parallel=args.ddp, parallel_dim=1 if args.rnn else 0) elif isinstance(action_space, gym.spaces.MultiDiscrete): pol = MultiCategoricalPol(observation_space, action_space, pol_net, args.rnn, data_parallel=args.ddp, parallel_dim=1 if args.rnn else 0) else: raise ValueError('Only Box, Discrete, and MultiDiscrete are supported') if args.rnn: vf_net = VNetLSTM(observation_space, h_size=256, cell_size=256) else: