def __init__(self, obs_space, action_space, model_dir, device=None, argmax=False, num_envs=1, use_memory=False, use_text=False): obs_space, self.preprocess_obss = utils.get_obss_preprocessor( obs_space) self.acmodel = ACModel(obs_space, action_space, use_memory=use_memory, use_text=use_text) self.device = device self.argmax = argmax self.num_envs = num_envs if self.acmodel.recurrent: self.memories = torch.zeros(self.num_envs, self.acmodel.memory_size) self.acmodel.load_state_dict(utils.get_model_state(model_dir)) self.acmodel.to(self.device) self.acmodel.eval() if hasattr(self.preprocess_obss, "vocab"): self.preprocess_obss.vocab.load_vocab(utils.get_vocab(model_dir))
def __init__(self, env_id, obs_space, model_dir, argmax=False, num_envs=1): _, self.preprocess_obss = utils.get_obss_preprocessor( env_id, obs_space, model_dir) self.acmodel = utils.load_model(model_dir) self.argmax = argmax self.num_envs = num_envs self.device = torch.device( "cuda" if torch.cuda.is_available() else "cpu")
def __init__(self, obs_space, action_space, model_dir, device=None, argmax=False, num_envs=1): obs_space, self.preprocess_obss = utils.get_obss_preprocessor(obs_space) self.acmodel = ACModel(obs_space, action_space) self.device = device self.argmax = argmax self.num_envs = num_envs self.acmodel.load_state_dict(utils.get_model_state(model_dir)) self.acmodel.to(self.device) self.acmodel.eval()
def __init__(self, env_id, obs_space, model_dir, argmax=False, num_envs=1): _, self.preprocess_obss = utils.get_obss_preprocessor( env_id, obs_space, model_dir) self.base_model = utils.load_model(model_dir) self.argmax = argmax self.num_envs = num_envs self.device = torch.device( "cuda" if torch.cuda.is_available() else "cpu") if self.base_model.recurrent: self.memories = torch.zeros(self.num_envs, self.base_model.memory_size)
def __init__(self, obs_space, action_space, model_dir, seed, n_columns, device=None, argmax=False, num_envs=1, use_memory=False, use_text=False): obs_space, self.preprocess_obss = utils.get_obss_preprocessor( obs_space) #self.acmodel = PNNModel(obs_space, action_space, use_memory=use_memory, use_text=use_text) self.acmodel = PNNModel(obs_space, action_space, use_memory=use_memory, use_text=use_text, use_pnn=True, base=None) self.device = device self.argmax = argmax self.num_envs = num_envs if self.acmodel.recurrent: self.memories = torch.zeros(self.num_envs, self.acmodel.memory_size) # Add a new column to the model for _ in range(n_columns): self.acmodel.base.new_task() # Load model parameters for PREVIOUS columns #for i in range(n_columns - 1): #utils.pnn_load_state_dict(self.acmodel, i, pnn_paths[i]) # Freeze the weights all previous columns #acmodel.base.freeze_columns(skip=[args.n_columns - 1]) # load CURRENT column model parameter for resuming training #if "model_state" in status: # acmodel.base.columns[args.n_columns - 1].load_state_dict(status["model_state"]) #status_path = utils.get_status_path(model_dir, args.seed) #utils.pnn_load_state_dict(acmodel, args.n_columns - 1, status_path) # Load model parameters for all columns status_path = utils.get_status_path(model_dir, seed) for i in range(n_columns - 1): utils.pnn_load_state_dict(self.acmodel, i, status_path) self.acmodel.to(self.device) self.acmodel.eval() if hasattr(self.preprocess_obss, "vocab"): self.preprocess_obss.vocab.load_vocab( utils.get_vocab(model_dir, seed))
def __init__(self, obs_space, action_space, model_dir, device=None, argmax=False, num_envs=1): obs_space, self.preprocess_obss = utils.get_obss_preprocessor(obs_space) self.acmodel = ACModel(obs_space, action_space) self.device = device self.argmax = argmax self.num_envs = num_envs if self.acmodel.recurrent: self.memories = torch.zeros(self.num_envs, self.acmodel.memory_size) self.acmodel.load_state_dict(utils.get_model_state(model_dir)) self.acmodel.to(self.device) self.acmodel.eval()
def __init__(self, obs_space, action_space, model_dir, device=None, argmax=False, num_envs=1): obs_space, self.preprocess_obss = utils.get_obss_preprocessor( obs_space) self.model = QModel(obs_space, action_space) self.device = device self.argmax = argmax self.num_envs = num_envs self.model.load_state_dict(utils.get_model_state(model_dir)) self.model.to(self.device) self.model.eval() if hasattr(self.preprocess_obss, "vocab"): self.preprocess_obss.vocab.load_vocab(utils.get_vocab(model_dir))
def __init__(self, env, obs_space, action_space, model_dir, ignoreLTL, progression_mode, gnn, recurrence=1, dumb_ac=False, device=None, argmax=False, num_envs=1): try: print(model_dir) status = utils.get_status(model_dir) except OSError: status = {"num_frames": 0, "update": 0} using_gnn = (gnn != "GRU" and gnn != "LSTM") obs_space, self.preprocess_obss = utils.get_obss_preprocessor( env, using_gnn, progression_mode) if "vocab" in status and self.preprocess_obss.vocab is not None: self.preprocess_obss.vocab.load_vocab(status["vocab"]) if recurrence > 1: self.acmodel = RecurrentACModel(env, obs_space, action_space, ignoreLTL, gnn, dumb_ac, True) self.memories = torch.zeros(num_envs, self.acmodel.memory_size, device=device) else: self.acmodel = ACModel(env, obs_space, action_space, ignoreLTL, gnn, dumb_ac, True) self.device = device self.argmax = argmax self.num_envs = num_envs self.acmodel.load_state_dict(utils.get_model_state(model_dir)) self.acmodel.to(self.device) self.acmodel.eval()
def __init__(self, obs_space, action_space, model_dir, model_name='AC', device=None, argmax=False, num_envs=1, use_memory=False, use_text=False, input_type="image", feature_learn="curiosity"): obs_space, self.preprocess_obss = utils.get_obss_preprocessor( obs_space) if model_name == 'ac': self.acmodel = ACModel(obs_space, action_space, use_memory=use_memory, use_text=use_text) elif model_name == 'sr': self.acmodel = SRModel(obs_space, action_space, input_type=input_type, use_memory=use_memory, use_text=use_text, feature_learn=feature_learn) self.model_name = model_name self.device = device self.argmax = argmax self.num_envs = num_envs if self.acmodel.recurrent: self.memories = torch.zeros(self.num_envs, self.acmodel.memory_size) self.acmodel.load_state_dict(utils.get_model_state(model_dir)) self.acmodel.to(self.device) self.acmodel.eval() if hasattr(self.preprocess_obss, "vocab"): self.preprocess_obss.vocab.load_vocab(utils.get_vocab(model_dir))
envs = [] for i in range(args.procs): envs.append(utils.make_env(args.env, args.seed + 10000 * i)) txt_logger.info("Environments loaded\n") # Load training status try: status = utils.get_status(model_dir) except OSError: status = {"num_frames": 0, "update": 0} txt_logger.info("Training status loaded\n") # Load observations preprocessor obs_space, preprocess_obss = utils.get_obss_preprocessor( envs[0].observation_space) # TODO if "vocab" in status: preprocess_obss.vocab.load_vocab(status["vocab"]) txt_logger.info("Observations preprocessor loaded") # Load model acmodel = ACModel(obs_space, envs[0].action_space, args.mem, args.text) if "model_state" in status: acmodel.load_state_dict(status["model_state"]) acmodel.to(device) txt_logger.info("Model loaded\n") txt_logger.info("{}\n".format(acmodel)) # Load algo
def main(env_name, seed, meta, load_id, procs, fullObs, POfullObs, frames, log_interval, save_interval, experimental, _run): """Main function. Called by sacred with arguments filled in from default.yaml or command line. """ # Make a bunch of experimental options available everywhere for easy change for cfg in experimental: setattr(exp_config, cfg, experimental[cfg]) cuda = torch.cuda.is_available() device = torch.device("cuda" if cuda else "cpu") model_name = meta['label'] + "_{}".format(_run._id) model_dir = utils.get_model_dir(model_name) # Define logger, CSV writer and Tensorboard writer logger = utils.get_logger(model_dir) csv_file, csv_writer = utils.get_csv_writer(model_dir) # Log command and all script arguments logger.info("{}\n".format(" ".join(sys.argv))) # Set seed for all randomness sources utils.seed(seed) # Generate environments envs = [] for i in range(procs): env = gym.make(env_name) env.seed(seed + 10000 * i) if fullObs: env = gym_minigrid.wrappers.FullyObsWrapper(env) elif POfullObs: env = gym_minigrid.wrappers.PartialObsFullGridWrapper(env) envs.append(env) # Define obss preprocessor obs_space, preprocess_obss = utils.get_obss_preprocessor( env_name, envs[0].observation_space, model_dir) # Load training status if load_id is not None: model1, model2, status = utils.load_status_and_model_from_db( db_uri, db_name, model_dir, load_id) if model1 is not None: model1 = model1.to(device) model2 = model2.to(device) acmodels = model1, model2 current_cycle_count, _ = scheduling(status['num_frames']) logger.info("Model successfully loaded\n") logger.info("Loaded status: {}".format(status)) else: # First one is pi_old, second one is pi_train acmodels = [None, create_model(obs_space, envs)] status = {"num_frames": 0, "update": 0} current_cycle_count = 0 logger.info("Model successfully created\n") logger.info("{}\n".format(acmodels[0])) logger.info("Used device: {}\n".format(device)) # Define actor-critic algo algo = create_algo(envs, *acmodels, preprocess_obss) # Train model num_frames = status["num_frames"] total_start_time = time.time() update = status["update"] # current_cycle_count = 0 while num_frames < frames: # Update model parameters cycle_count, alpha = scheduling(num_frames) if cycle_count != current_cycle_count: current_cycle_count = cycle_count switch_training_model(algo, obs_space, envs) logger.info("Switched training model") update_start_time = time.time() logs = algo.update_parameters(alpha) update_end_time = time.time() num_frames += logs["num_frames"] update += 1 # Print logs if update % log_interval == 0: fps = logs["num_frames"] / (update_end_time - update_start_time) duration = int(time.time() - total_start_time) return_per_episode = utils.synthesize(logs["return_per_episode"]) rreturn_per_episode = utils.synthesize( logs["reshaped_return_per_episode"]) num_frames_per_episode = utils.synthesize( logs["num_frames_per_episode"]) header = ["update", "frames", "FPS", "duration"] data = [update, num_frames, fps, duration] header += ["rreturn_" + key for key in rreturn_per_episode.keys()] data += rreturn_per_episode.values() header += [ "num_frames_" + key for key in num_frames_per_episode.keys() ] data += num_frames_per_episode.values() header += [ "entropy", "value_train", "value_old", "policy_loss_train", "policy_loss_old", "value_loss_train", "value_loss_old" ] data += [ logs["entropy"], logs["value_train"], logs["value_old"], logs["policy_loss_train"], logs["policy_loss_old"], logs["value_loss_train"], logs["value_loss_old"] ] header += [ "grad_norm_train", "grad_norm_old", "alpha", "reg_loss_policy", "reg_loss_value" ] data += [ logs["grad_norm_train"], logs["grad_norm_old"], alpha, logs["reg_loss_policy"], logs["reg_loss_value"] ] logger.info( "U {} | F {:06} | FPS {:04.0f} | D {} | rR:μσmM {:.2f} {:.2f} {:.2f} {:.2f} | F:μσmM {:.1f} {:.1f} {} {} | H {:.3f} | V:to {:.3f} {:.3f} " .format(*data[:15])) logger.info( "pL:to {:.3f} {:.3f} | vL:to {:.3f} {:.3f} | ∇:to {:.3f} {:.3f} | alpha {:.2f} | rLpv {:.3f} {:.3f}\n" .format(*data[15:])) header += ["return_" + key for key in return_per_episode.keys()] data += return_per_episode.values() if status["num_frames"] == 0: csv_writer.writerow(header) csv_writer.writerow(data) csv_file.flush() for head, dat in zip(header, data): _run.log_scalar(head, dat, num_frames) status = {"num_frames": num_frames, "update": update} # Save vocabulary and model if save_interval > 0 and update % save_interval == 0: preprocess_obss.vocab.save() utils.save_model(algo.pi_old, algo.pi_train, model_dir) logger.info("Model successfully saved") utils.save_status(status, model_dir) utils.save_model_to_db(algo.pi_old, algo.pi_train, model_dir, num_frames, _run) utils.save_status_to_db({ "num_frames": num_frames, "update": update }, model_dir, num_frames, _run)
def main(): # Parse arguments parser = argparse.ArgumentParser() ## General parameters parser.add_argument( "--algo", required=True, help="algorithm to use: a2c | ppo | ppo_intrinsic (REQUIRED)") parser.add_argument("--env", required=True, help="name of the environment to train on (REQUIRED)") parser.add_argument( "--model", default=None, help="name of the model (default: {ENV}_{ALGO}_{TIME})") parser.add_argument("--seed", type=int, default=1, help="random seed (default: 1)") parser.add_argument("--log-interval", type=int, default=1, help="number of updates between two logs (default: 1)") parser.add_argument( "--save-interval", type=int, default=10, help= "number of updates between two saves (default: 10, 0 means no saving)") parser.add_argument("--procs", type=int, default=16, help="number of processes (default: 16)") parser.add_argument("--frames", type=int, default=10**7, help="number of frames of training (default: 1e7)") ## Parameters for main algorithm parser.add_argument("--epochs", type=int, default=4, help="number of epochs for PPO (default: 4)") parser.add_argument("--batch-size", type=int, default=256, help="batch size for PPO (default: 256)") parser.add_argument( "--frames-per-proc", type=int, default=None, help= "number of frames per process before update (default: 5 for A2C and 128 for PPO)" ) parser.add_argument("--discount", type=float, default=0.99, help="discount factor (default: 0.99)") parser.add_argument("--lr", type=float, default=0.001, help="learning rate (default: 0.001)") parser.add_argument( "--gae-lambda", type=float, default=0.95, help="lambda coefficient in GAE formula (default: 0.95, 1 means no gae)" ) parser.add_argument("--entropy-coef", type=float, default=0.01, help="entropy term coefficient (default: 0.01)") parser.add_argument("--value-loss-coef", type=float, default=0.5, help="value loss term coefficient (default: 0.5)") parser.add_argument("--max-grad-norm", type=float, default=0.5, help="maximum norm of gradient (default: 0.5)") parser.add_argument( "--optim-eps", type=float, default=1e-8, help="Adam and RMSprop optimizer epsilon (default: 1e-8)") parser.add_argument("--optim-alpha", type=float, default=0.99, help="RMSprop optimizer alpha (default: 0.99)") parser.add_argument("--clip-eps", type=float, default=0.2, help="clipping epsilon for PPO (default: 0.2)") parser.add_argument( "--recurrence", type=int, default=1, help= "number of time-steps gradient is backpropagated (default: 1). If > 1, a LSTM is added to the model to have memory." ) parser.add_argument("--text", action="store_true", default=False, help="add a GRU to the model to handle text input") parser.add_argument("--visualize", default=False, help="show real time CNN layer weight changes") args = parser.parse_args() args.mem = args.recurrence > 1 # Set run dir date = datetime.datetime.now().strftime("%y-%m-%d-%H-%M-%S") default_model_name = f"{args.env}_{args.algo}_seed{args.seed}_{date}" model_name = args.model or default_model_name model_dir = utils.get_model_dir(model_name) # Load loggers and Tensorboard writer txt_logger = utils.get_txt_logger(model_dir) csv_file, csv_logger = utils.get_csv_logger(model_dir) tb_writer = tensorboardX.SummaryWriter(model_dir) # Log command and all script arguments txt_logger.info("{}\n".format(" ".join(sys.argv))) txt_logger.info("{}\n".format(args)) # Set seed for all randomness sources utils.seed(args.seed) # Set device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") txt_logger.info(f"Device: {device}\n") # Load environments envs = [] for i in range(args.procs): envs.append(utils.make_env(args.env, args.seed + 10000 * i)) txt_logger.info("Environments loaded\n") # Load training status try: status = utils.get_status(model_dir) except OSError: status = {"num_frames": 0, "update": 0} txt_logger.info("Training status loaded\n") # Load observations preprocessor obs_space, preprocess_obss = utils.get_obss_preprocessor( envs[0].observation_space) if "vocab" in status: preprocess_obss.vocab.load_vocab(status["vocab"]) txt_logger.info("Observations preprocessor loaded") # Load model acmodel = ACModel(obs_space, envs[0].action_space, args.mem, args.text) if "model_state" in status: acmodel.load_state_dict(status["model_state"]) acmodel.to(device) txt_logger.info("Model loaded\n") txt_logger.info("{}\n".format(acmodel)) # Load algo if args.algo == "a2c": algo = torch_ac.A2CAlgo(envs, acmodel, device, args.frames_per_proc, args.discount, args.lr, args.gae_lambda, args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence, args.optim_alpha, args.optim_eps, preprocess_obss) elif args.algo == "ppo": algo = torch_ac.PPOAlgo(envs, acmodel, device, args.frames_per_proc, args.discount, args.lr, args.gae_lambda, args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence, args.optim_eps, args.clip_eps, args.epochs, args.batch_size, preprocess_obss) elif args.algo == "ppo_intrinsic": algo = torch_ac.PPOAlgoIntrinsic( envs, acmodel, device, args.frames_per_proc, args.discount, args.lr, args.gae_lambda, args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence, args.optim_eps, args.clip_eps, args.epochs, args.batch_size, preprocess_obss) elif args.algo == "a2c_intrinsic": algo = torch_ac.A2CAlgoIntrinsic( envs, acmodel, device, args.frames_per_proc, args.discount, args.lr, args.gae_lambda, args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence, args.optim_alpha, args.optim_eps, preprocess_obss) else: raise ValueError("Incorrect algorithm name: {}".format(args.algo)) if "optimizer_state" in status: algo.optimizer.load_state_dict(status["optimizer_state"]) txt_logger.info("Optimizer loaded\n") # Train model num_frames = status["num_frames"] update = status["update"] start_time = time.time() print_visual = args.visualize if print_visual: fig, axs = plt.subplots(1, 3) fig.suptitle('Convolution Layer Weights Normalized Difference') while num_frames < args.frames: # Store copies of s_t model params old_parameters = {} for name, param in acmodel.named_parameters(): old_parameters[name] = param.detach().numpy().copy() # Update model parameters update_start_time = time.time() exps, logs1 = algo.collect_experiences() logs2 = algo.update_parameters(exps) logs = {**logs1, **logs2} update_end_time = time.time() # Store copies of s_t+1 model params new_parameters = {} for name, param in acmodel.named_parameters(): new_parameters[name] = param.detach().numpy().copy() # Compute L2 Norm of model state differences # Print model weight change visualization for index in range(len(old_parameters.keys())): if index == 0 or index == 2 or index == 4: key = list(old_parameters.keys())[index] old_weights = old_parameters[key] new_weights = new_parameters[key] norm_diff = numpy.linalg.norm(new_weights - old_weights) diff_matrix = abs(new_weights - old_weights) diff_matrix[:, :, 0, 0] = normalize(diff_matrix[:, :, 0, 0], norm='max', axis=0) if print_visual: axs[int(index / 2)].imshow(diff_matrix[:, :, 0, 0], cmap='Greens', interpolation='nearest') # This allows the plots to update as the model trains if print_visual: plt.ion() plt.show() plt.pause(0.001) num_frames += logs["num_frames"] update += 1 # Print logs if update % args.log_interval == 0: fps = logs["num_frames"] / (update_end_time - update_start_time) duration = int(time.time() - start_time) return_per_episode = utils.synthesize(logs["return_per_episode"]) rreturn_per_episode = utils.synthesize( logs["reshaped_return_per_episode"]) num_frames_per_episode = utils.synthesize( logs["num_frames_per_episode"]) header = ["update", "frames", "FPS", "duration"] data = [update, num_frames, fps, duration] header += ["rreturn_" + key for key in rreturn_per_episode.keys()] data += rreturn_per_episode.values() header += [ "num_frames_" + key for key in num_frames_per_episode.keys() ] data += num_frames_per_episode.values() header += [ "entropy", "value", "policy_loss", "value_loss", "grad_norm" ] data += [ logs["entropy"], logs["value"], logs["policy_loss"], logs["value_loss"], logs["grad_norm"] ] txt_logger.info( "U {} | F {:06} | FPS {:04.0f} | D {} | rR:μσmM {:.2f} {:.2f} {:.2f} {:.2f} | F:μσmM {:.1f} {:.1f} {} {} | H {:.3f} | V {:.3f} | pL {:.3f} | vL {:.3f} | ∇ {:.3f}" .format(*data)) header += ["return_" + key for key in return_per_episode.keys()] data += return_per_episode.values() if status["num_frames"] == 0: csv_logger.writerow(header) csv_logger.writerow(data) csv_file.flush() for field, value in zip(header, data): tb_writer.add_scalar(field, value, num_frames) # Save status if args.save_interval > 0 and update % args.save_interval == 0: status = { "num_frames": num_frames, "update": update, "model_state": acmodel.state_dict(), "optimizer_state": algo.optimizer.state_dict() } if hasattr(preprocess_obss, "vocab"): status["vocab"] = preprocess_obss.vocab.vocab utils.save_status(status, model_dir) txt_logger.info("Status saved")
def tuner(icm_lr, reward_weighting, normalise_rewards, args): import argparse import datetime import torch import torch_ac import tensorboardX import sys import numpy as np from model import ACModel from .a2c import A2CAlgo # from .ppo import PPOAlgo frames_to_visualise = 200 # Parse arguments args.mem = args.recurrence > 1 def make_exploration_heatmap(args, plot_title): import numpy as np import matplotlib.pyplot as plt visitation_counts = np.load( f"{args.model}_visitation_counts.npy", allow_pickle=True ) plot_title = str(np.count_nonzero(visitation_counts)) + args.model plt.imshow(np.log(visitation_counts)) plt.colorbar() plt.title(plot_title) plt.savefig(f"{plot_title}_visitation_counts.png") # Set run dir date = datetime.datetime.now().strftime("%y-%m-%d-%H-%M-%S") default_model_name = f"{args.env}_{args.algo}_seed{args.seed}_{date}" model_name = args.model or default_model_name model_dir = utils.get_model_dir(model_name) # Load loggers and Tensorboard writer txt_logger = utils.get_txt_logger(model_dir) csv_file, csv_logger = utils.get_csv_logger(model_dir) tb_writer = tensorboardX.SummaryWriter(model_dir) # Log command and all script arguments txt_logger.info("{}\n".format(" ".join(sys.argv))) txt_logger.info("{}\n".format(args)) # Set seed for all randomness sources utils.seed(args.seed) # Set device device = "cpu" # torch.device("cuda" if torch.cuda.is_available() else "cpu") txt_logger.info(f"Device: {device}\n") # Load environments envs = [] for i in range(16): an_env = utils.make_env( args.env, int(args.frames_before_reset), int(args.environment_seed) ) envs.append(an_env) txt_logger.info("Environments loaded\n") # Load training status try: status = utils.get_status(model_dir) except OSError: status = {"num_frames": 0, "update": 0} txt_logger.info("Training status loaded\n") # Load observations preprocessor obs_space, preprocess_obss = utils.get_obss_preprocessor(envs[0].observation_space) if "vocab" in status: preprocess_obss.vocab.load_vocab(status["vocab"]) txt_logger.info("Observations preprocessor loaded") # Load model acmodel = ACModel(obs_space, envs[0].action_space, args.mem, args.text) if "model_state" in status: acmodel.load_state_dict(status["model_state"]) acmodel.to(device) txt_logger.info("Model loaded\n") txt_logger.info("{}\n".format(acmodel)) # Load algo # adapted from impact driven RL from .models import AutoencoderWithUncertainty autoencoder = AutoencoderWithUncertainty(observation_shape=(7, 7, 3)).to(device) autoencoder_opt = torch.optim.Adam( autoencoder.parameters(), lr=icm_lr, weight_decay=0 ) if args.algo == "a2c": algo = A2CAlgo( envs, acmodel, autoencoder, autoencoder_opt, args.uncertainty, args.noisy_tv, args.curiosity, args.randomise_env, args.uncertainty_budget, args.environment_seed, reward_weighting, normalise_rewards, args.frames_before_reset, device, args.frames_per_proc, args.discount, args.lr, args.gae_lambda, args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence, args.optim_alpha, args.optim_eps, preprocess_obss, None, args.random_action, ) elif args.algo == "ppo": algo = PPOAlgo( envs, acmodel, autoencoder, autoencoder_opt, args.uncertainty, args.noisy_tv, args.curiosity, args.randomise_env, args.uncertainty_budget, args.environment_seed, reward_weighting, normalise_rewards, device, args.frames_per_proc, args.discount, args.lr, args.gae_lambda, args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence, args.optim_eps, args.clip_eps, args.epochs, args.batch_size, preprocess_obss, ) else: raise ValueError("Incorrect algorithm name: {}".format(args.algo)) if "optimizer_state" in status: algo.optimizer.load_state_dict(status["optimizer_state"]) txt_logger.info("Optimizer loaded\n") # Train model num_frames = status["num_frames"] update = status["update"] start_time = time.time() while num_frames < args.frames: # Update model parameters update_start_time = time.time() exps, logs1 = algo.collect_experiences() logs2 = algo.update_parameters(exps) logs = {**logs1, **logs2} update_end_time = time.time() num_frames += logs["num_frames"] update += 1 log_to_wandb(logs, start_time, update_start_time, update_end_time) # Print logs if update % args.log_interval == 0: fps = logs["num_frames"] / (update_end_time - update_start_time) duration = int(time.time() - start_time) return_per_episode = utils.synthesize(logs["return_per_episode"]) rreturn_per_episode = utils.synthesize(logs["reshaped_return_per_episode"]) num_frames_per_episode = utils.synthesize(logs["num_frames_per_episode"]) header = ["update", "frames", "FPS", "duration"] data = [update, num_frames, fps, duration] header += ["rreturn_" + key for key in rreturn_per_episode.keys()] data += rreturn_per_episode.values() header += ["num_frames_" + key for key in num_frames_per_episode.keys()] data += num_frames_per_episode.values() header += [ "intrinsic_rewards", "uncertainties", "novel_states_visited", "entropy", "value", "policy_loss", "value_loss", "grad_norm", ] data += [ logs["intrinsic_rewards"].mean().item(), logs["uncertainties"].mean().item(), logs["novel_states_visited"].mean().item(), logs["entropy"], logs["value"], logs["policy_loss"], logs["value_loss"], logs["grad_norm"], ] txt_logger.info( "U {} | F {:06} | FPS {:04.0f} | D {} | rR:μσmM {:.2f} {:.2f} {:.2f} {:.2f} | F:μσmM {:.1f} {:.1f} {} {} | H {:.3f} | V {:.3f} | pL {:.3f}".format( *data ) ) # Save status if args.save_interval > 0 and update % args.save_interval == 0: status = { "num_frames": num_frames, "update": update, "model_state": acmodel.state_dict(), "optimizer_state": algo.optimizer.state_dict(), } if hasattr(preprocess_obss, "vocab"): status["vocab"] = preprocess_obss.vocab.vocab utils.save_status(status, model_dir) return
def main(raw_args=None): # Parse arguments parser = argparse.ArgumentParser() ## General parameters parser.add_argument("--algo", required=True, help="algorithm to use: a2c | ppo | ipo (REQUIRED)") parser.add_argument("--domain1", required=True, help="name of the first domain to train on (REQUIRED)") parser.add_argument( "--domain2", required=True, help="name of the second domain to train on (REQUIRED)") parser.add_argument( "--p1", required=True, type=float, help="Proportion of training environments from first domain (REQUIRED)" ) parser.add_argument("--model", required=True, help="name of the model") parser.add_argument("--seed", type=int, default=1, help="random seed (default: 1)") parser.add_argument("--log-interval", type=int, default=1, help="number of updates between two logs (default: 1)") parser.add_argument( "--save-interval", type=int, default=10, help= "number of updates between two saves (default: 10, 0 means no saving)") parser.add_argument("--procs", type=int, default=16, help="number of processes (default: 16)") parser.add_argument("--frames", type=int, default=10**7, help="number of frames of training (default: 1e7)") ## Parameters for main algorithm parser.add_argument("--epochs", type=int, default=4, help="number of epochs for PPO (default: 4)") parser.add_argument("--batch-size", type=int, default=256, help="batch size for PPO (default: 256)") parser.add_argument( "--frames-per-proc", type=int, default=None, help= "number of frames per process before update (default: 5 for A2C and 128 for PPO)" ) parser.add_argument("--discount", type=float, default=0.99, help="discount factor (default: 0.99)") parser.add_argument("--lr", type=float, default=0.001, help="learning rate (default: 0.001)") parser.add_argument( "--gae-lambda", type=float, default=0.95, help="lambda coefficient in GAE formula (default: 0.95, 1 means no gae)" ) parser.add_argument("--entropy-coef", type=float, default=0.01, help="entropy term coefficient (default: 0.01)") parser.add_argument("--value-loss-coef", type=float, default=0.5, help="value loss term coefficient (default: 0.5)") parser.add_argument("--max-grad-norm", type=float, default=0.5, help="maximum norm of gradient (default: 0.5)") parser.add_argument( "--optim-eps", type=float, default=1e-8, help="Adam and RMSprop optimizer epsilon (default: 1e-8)") parser.add_argument("--optim-alpha", type=float, default=0.99, help="RMSprop optimizer alpha (default: 0.99)") parser.add_argument("--clip-eps", type=float, default=0.2, help="clipping epsilon for PPO (default: 0.2)") parser.add_argument( "--recurrence", type=int, default=1, help= "number of time-steps gradient is backpropagated (default: 1). If > 1, a LSTM is added to the model to have memory." ) parser.add_argument("--text", action="store_true", default=False, help="add a GRU to the model to handle text input") args = parser.parse_args(raw_args) args.mem = args.recurrence > 1 # Check PyTorch version if (torch.__version__ != '1.2.0'): raise ValueError( "PyTorch version must be 1.2.0 (see README). Your version is {}.". format(torch.__version__)) if args.mem: raise ValueError("Policies with memory not supported.") # Set run dir date = datetime.datetime.now().strftime("%y-%m-%d-%H-%M-%S") default_model_name = args.model model_name = args.model or default_model_name model_dir = utils.get_model_dir(model_name) # Load loggers and Tensorboard writer txt_logger = utils.get_txt_logger(model_dir) csv_file, csv_logger = utils.get_csv_logger(model_dir) tb_writer = tensorboardX.SummaryWriter(model_dir) # Log command and all script arguments txt_logger.info("{}\n".format(" ".join(sys.argv))) txt_logger.info("{}\n".format(args)) # Set seed for all randomness sources torch.backends.cudnn.deterministic = True utils.seed(args.seed) # Set device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") txt_logger.info(f"Device: {device}\n") # Load environments from different domains domain1 = args.domain1 # e.g., 'MiniGrid-ColoredKeysRed-v0' domain2 = args.domain2 # e.g., 'MiniGrid-ColoredKeysYellow-v0' p1 = args.p1 # Proportion of environments from domain1 num_envs_total = args.procs # Total number of environments num_domain1 = math.ceil( p1 * num_envs_total) # Number of environments in domain1 num_domain2 = num_envs_total - num_domain1 # Number of environments in domain2 # Environments from domain1 envs1 = [] for i in range(num_domain1): envs1.append(utils.make_env(domain1, args.seed + 10000 * i)) # Environments from domain2 envs2 = [] for i in range(num_domain2): envs2.append(utils.make_env(domain2, args.seed + 10000 * i)) # All environments envs = envs1 + envs2 txt_logger.info("Environments loaded\n") # Load training status try: status = utils.get_status(model_dir) except OSError: status = {"num_frames": 0, "update": 0} txt_logger.info("Training status loaded\n") # Load observations preprocessor obs_space, preprocess_obss = utils.get_obss_preprocessor( envs[0].observation_space) if "vocab" in status: preprocess_obss.vocab.load_vocab(status["vocab"]) txt_logger.info("Observations preprocessor loaded") if args.algo == "ipo": # Load model for IPO game acmodel = ACModel_average(obs_space, envs[0].action_space, args.mem, args.text) if "model_state" in status: acmodel.load_state_dict(status["model_state"]) acmodel.to(device) txt_logger.info("Model loaded\n") txt_logger.info("{}\n".format(acmodel)) else: # Load model (for standard PPO or A2C) acmodel = ACModel(obs_space, envs[0].action_space, args.mem, args.text) if "model_state" in status: acmodel.load_state_dict(status["model_state"]) acmodel.to(device) txt_logger.info("Model loaded\n") txt_logger.info("{}\n".format(acmodel)) # Load algo if args.algo == "a2c": algo = torch_ac.A2CAlgo(envs, acmodel, device, args.frames_per_proc, args.discount, args.lr, args.gae_lambda, args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence, args.optim_alpha, args.optim_eps, preprocess_obss) if "optimizer_state" in status: algo.optimizer.load_state_dict(status["optimizer_state"]) txt_logger.info("Optimizer loaded\n") elif args.algo == "ppo": algo = torch_ac.PPOAlgo(envs, acmodel, device, args.frames_per_proc, args.discount, args.lr, args.gae_lambda, args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence, args.optim_eps, args.clip_eps, args.epochs, args.batch_size, preprocess_obss) if "optimizer_state" in status: algo.optimizer.load_state_dict(status["optimizer_state"]) txt_logger.info("Optimizer loaded\n") elif args.algo == "ipo": # One algo per domain. These have different envivonments, but shared acmodel algo1 = torch_ac.IPOAlgo( envs1, acmodel, 1, device, args.frames_per_proc, args.discount, args.lr, args.gae_lambda, args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence, args.optim_eps, args.clip_eps, args.epochs, args.batch_size, preprocess_obss) algo2 = torch_ac.IPOAlgo( envs2, acmodel, 2, device, args.frames_per_proc, args.discount, args.lr, args.gae_lambda, args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence, args.optim_eps, args.clip_eps, args.epochs, args.batch_size, preprocess_obss) if "optimizer_state1" in status: algo1.optimizer.load_state_dict(status["optimizer_state1"]) txt_logger.info("Optimizer 1 loaded\n") if "optimizer_state2" in status: algo2.optimizer.load_state_dict(status["optimizer_state2"]) txt_logger.info("Optimizer 2 loaded\n") else: raise ValueError("Incorrect algorithm name: {}".format(args.algo)) # Train model num_frames = status["num_frames"] update = status["update"] start_time = time.time() while num_frames < args.frames: # Update model parameters update_start_time = time.time() if args.algo == "ipo": # Standard method # Collect experiences on first domain exps1, logs_exps1 = algo1.collect_experiences() # Update params of model corresponding to first domain logs_algo1 = algo1.update_parameters(exps1) # Collect experiences on second domain exps2, logs_exps2 = algo2.collect_experiences() # Update params of model corresponding to second domain logs_algo2 = algo2.update_parameters(exps2) # Update end time update_end_time = time.time() # Combine logs logs_exps = { 'return_per_episode': logs_exps1["return_per_episode"] + logs_exps2["return_per_episode"], 'reshaped_return_per_episode': logs_exps1["reshaped_return_per_episode"] + logs_exps2["reshaped_return_per_episode"], 'num_frames_per_episode': logs_exps1["num_frames_per_episode"] + logs_exps2["num_frames_per_episode"], 'num_frames': logs_exps1["num_frames"] + logs_exps2["num_frames"] } logs_algo = { 'entropy': (num_domain1 * logs_algo1["entropy"] + num_domain2 * logs_algo2["entropy"]) / num_envs_total, 'value': (num_domain1 * logs_algo1["value"] + num_domain2 * logs_algo2["value"]) / num_envs_total, 'policy_loss': (num_domain1 * logs_algo1["policy_loss"] + num_domain2 * logs_algo2["policy_loss"]) / num_envs_total, 'value_loss': (num_domain1 * logs_algo1["value_loss"] + num_domain2 * logs_algo2["value_loss"]) / num_envs_total, 'grad_norm': (num_domain1 * logs_algo1["grad_norm"] + num_domain2 * logs_algo2["grad_norm"]) / num_envs_total } logs = {**logs_exps, **logs_algo} num_frames += logs["num_frames"] else: exps, logs1 = algo.collect_experiences() logs2 = algo.update_parameters(exps) logs = {**logs1, **logs2} update_end_time = time.time() num_frames += logs["num_frames"] update += 1 # Print logs if update % args.log_interval == 0: fps = logs["num_frames"] / (update_end_time - update_start_time) duration = int(time.time() - start_time) return_per_episode = utils.synthesize(logs["return_per_episode"]) rreturn_per_episode = utils.synthesize( logs["reshaped_return_per_episode"]) num_frames_per_episode = utils.synthesize( logs["num_frames_per_episode"]) header = ["update", "frames", "FPS", "duration"] data = [update, num_frames, fps, duration] header += ["rreturn_" + key for key in rreturn_per_episode.keys()] data += rreturn_per_episode.values() header += [ "num_frames_" + key for key in num_frames_per_episode.keys() ] data += num_frames_per_episode.values() header += [ "entropy", "value", "policy_loss", "value_loss", "grad_norm" ] data += [ logs["entropy"], logs["value"], logs["policy_loss"], logs["value_loss"], logs["grad_norm"] ] txt_logger.info( "U {} | F {:06} | FPS {:04.0f} | D {} | rR:μσmM {:.2f} {:.2f} {:.2f} {:.2f} | F:μσmM {:.1f} {:.1f} {} {} | H {:.3f} | V {:.3f} | pL {:.3f} | vL {:.3f} | ∇ {:.3f}" .format(*data)) header += ["return_" + key for key in return_per_episode.keys()] data += return_per_episode.values() # header += ["debug_last_env_reward"] # data += [logs["debug_last_env_reward"]] header += ["total_loss"] data += [ logs["policy_loss"] - args.entropy_coef * logs["entropy"] + args.value_loss_coef * logs["value_loss"] ] if status["num_frames"] == 0: csv_logger.writerow(header) csv_logger.writerow(data) csv_file.flush() for field, value in zip(header, data): tb_writer.add_scalar(field, value, num_frames) # Save status if args.save_interval > 0 and update % args.save_interval == 0: if args.algo == "ipo": status = { "num_frames": num_frames, "update": update, "model_state": acmodel.state_dict(), "optimizer_state1": algo1.optimizer.state_dict(), "optimizer_state2": algo2.optimizer.state_dict() } else: status = { "num_frames": num_frames, "update": update, "model_state": acmodel.state_dict(), "optimizer_state": algo.optimizer.state_dict() } if hasattr(preprocess_obss, "vocab"): status["vocab"] = preprocess_obss.vocab.vocab utils.save_status(status, model_dir) txt_logger.info("Status saved")
def run(full_args: Namespace) -> None: args = full_args.main agent_args = full_args.agent model_args = full_args.model if args.seed == 0: args.seed = full_args.run_id + 1 max_eprews = args.max_eprews post_process_args(agent_args) post_process_args(model_args) model_dir = full_args.cfg_dir print(model_dir) # ============================================================================================== # Set seed for all randomness sources utils.seed(args.seed) # ============================================================================================== # Generate environment env = gym.make(args.env) env.max_steps = full_args.env_cfg.max_episode_steps env.seed(args.seed + 10000 * 0) env = gym_wrappers.RecordingBehaviour(env) # Define obss preprocessor max_image_value = full_args.env_cfg.max_image_value normalize_img = full_args.env_cfg.normalize obs_space, preprocess_obss = utils.get_obss_preprocessor( args.env, env.observation_space, model_dir, max_image_value=max_image_value, normalize=normalize_img) # ============================================================================================== # Load training status try: status = utils.load_status(model_dir) except OSError: status = {"num_frames": 0, "update": 0} saver = utils.SaveData(model_dir, save_best=args.save_best, save_all=args.save_all) model, agent_data, other_data = None, dict(), None try: # Continue from last point model, agent_data, other_data = saver.load_training_data(best=False) print("Training data exists & loaded successfully\n") except OSError: print("Could not load training data\n") if torch.cuda.is_available(): model.cuda() device = torch.device("cuda") else: model.cpu() device = torch.device("cpu") # ============================================================================================== # Test model done = False model.eval() initial_image = None if agent_args.name == 'PPORND': model = model.policy import argparse n_cfg = argparse.Namespace() viz = visualize_episode.VisualizeEpisode(n_cfg) obs = env.reset() memory = torch.zeros(1, model.memory_size, device=device) while True: if done: agent_behaviour = env.get_behaviour() nr_steps = agent_behaviour['step_count'] map_shape = np.array((agent_behaviour['full_states'].shape[1], agent_behaviour['full_states'].shape[2])) new_img = viz.draw_single_episode( initial_image, agent_behaviour['positions'][:nr_steps].astype(np.uint8), map_shape, agent_behaviour['actions'][:nr_steps].astype(np.uint8)) cv2.imshow("Map", new_img) cv2.waitKey(0) obs = env.reset() memory = torch.zeros(1, model.memory_size, device=device) time.sleep(0.1) renderer = env.render() if initial_image is None: initial_image = renderer.getArray() preprocessed_obs = preprocess_obss([obs], device=device) if model.recurrent: dist, _, memory = model(preprocessed_obs, memory) else: dist, value = model(preprocessed_obs) #action = dist.probs.argmax() action = dist.sample() obs, reward, done, _ = env.step(action.cpu().numpy()) if renderer.window is None: break
def run(full_args: Namespace, return_models: bool = False): if sys.argv[0].startswith("train"): import os full_args.out_dir = os.path.dirname(sys.argv[1]) args = full_args.main agent_args = full_args.agent model_args = full_args.model extra_logs = getattr(full_args, "extra_logs", None) main_r_key = getattr(full_args, "main_r_key", None) if args.seed == 0: args.seed = full_args.run_id + 1 max_eprews = args.max_eprews max_eprews_window = getattr(args, "max_eprews_window", 1) post_process_args(agent_args) post_process_args(model_args) model_dir = getattr(args, "model_dir", full_args.out_dir) print(model_dir) # ============================================================================================== # @ torc_rl repo original # Define logger, CSV writer and Tensorboard writer logger = utils.get_logger(model_dir) csv_file, csv_writer = utils.get_csv_writer(model_dir) tb_writer = None if args.tb: from tensorboardX import SummaryWriter tb_writer = SummaryWriter(model_dir) # Log command and all script arguments logger.info("{}\n".format(" ".join(sys.argv))) logger.info("{}\n".format(args)) # ============================================================================================== # Set seed for all randomness sources utils.seed(args.seed) # ============================================================================================== # Generate environments envs = [] # Get env wrappers - must be a list of elements wrapper_method = getattr(full_args.env_cfg, "wrapper", None) if wrapper_method is None: def idem(x): return x env_wrapper = idem else: env_wrappers = [getattr(gym_wrappers, w_p) for w_p in wrapper_method] def env_wrapp(w_env): for wrapper in env_wrappers[::-1]: w_env = wrapper(w_env) return w_env env_wrapper = env_wrapp actual_procs = getattr(args, "actual_procs", None) no_actions = getattr(full_args.env_cfg, "no_actions", 6) if actual_procs: # Split envs in chunks no_envs = args.procs envs, chunk_size = get_envs(full_args, env_wrapper, no_envs, n_actions=no_actions) first_env = envs[0][0] print( f"NO of envs / proc: {chunk_size}; No of processes {len(envs[1:])} + Master" ) else: for i in range(args.procs): env = env_wrapper(gym.make(args.env)) env.max_steps = full_args.env_cfg.max_episode_steps env.seed(args.seed + 10000 * i) envs.append(env) first_env = envs[0] # Generate evaluation envs eval_envs = [] eval_episodes = getattr(full_args.env_cfg, "eval_episodes", 0) if full_args.env_cfg.no_eval_envs > 0: no_envs = full_args.env_cfg.no_eval_envs eval_envs, chunk_size = get_envs(full_args, env_wrapper, no_envs, n_actions=no_actions) # Define obss preprocessor max_image_value = full_args.env_cfg.max_image_value normalize_img = full_args.env_cfg.normalize permute = getattr(full_args.env_cfg, "permute", False) obss_preprocessor = getattr(full_args.env_cfg, "obss_preprocessor", None) obs_space, preprocess_obss = utils.get_obss_preprocessor( args.env, first_env.observation_space, model_dir, max_image_value=max_image_value, normalize=normalize_img, permute=permute, type=obss_preprocessor) first_obs = first_env.reset() if "state" in first_obs: full_state_size = first_obs["state"].shape # Add full size shape add_to_cfg(full_args, MAIN_CFG_ARGS, "full_state_size", full_state_size) if "position" in first_obs: position_size = first_obs["position"].shape # Add full size shape add_to_cfg(full_args, MAIN_CFG_ARGS, "position_size", position_size) # Add the width and height of environment for position estimation model_args.width = first_env.unwrapped.width model_args.height = first_env.unwrapped.height # ============================================================================================== # Load training status try: status = utils.load_status(model_dir) except OSError: status = {"num_frames": 0, "update": 0} saver = utils.SaveData(model_dir, save_best=args.save_best, save_all=args.save_all) model, agent_data, other_data = None, dict(), None try: # Continue from last point model, agent_data, other_data = saver.load_training_data(best=False) logger.info("Training data exists & loaded successfully\n") except OSError: logger.info("Could not load training data\n") # ============================================================================================== # Load Model if model is None: model = get_model(model_args, obs_space, first_env.action_space, use_memory=model_args.mem) logger.info(f"Model [{model_args.name}] successfully created\n") # Print Model info logger.info("{}\n".format(model)) if torch.cuda.is_available(): model.cuda() logger.info("CUDA available: {}\n".format(torch.cuda.is_available())) # ============================================================================================== # Load Agent algo = get_agent(full_args.agent, envs, model, agent_data, preprocess_obss=preprocess_obss, reshape_reward=None, eval_envs=eval_envs, eval_episodes=eval_episodes) has_evaluator = hasattr(algo, "evaluate") and full_args.env_cfg.no_eval_envs > 0 if return_models: return algo, model, envs, saver # ============================================================================================== # Train model prev_rewards = [] crt_eprew = 0 if "eprew" in other_data: crt_eprew = other_data["eprew"] num_frames = status["num_frames"] total_start_time = time.time() update = status["update"] update_start_time = time.time() while num_frames < args.frames: # Update model parameters logs = algo.update_parameters() num_frames += logs["num_frames"] update += 1 if update % args.eval_interval == 0 and has_evaluator: eval_logs = algo.evaluate(eval_key=main_r_key) logs.update(eval_logs) prev_start_time = update_start_time update_start_time = time.time() # Print logs if update % args.log_interval == 0: fps = logs["num_frames"] / (update_start_time - prev_start_time) duration = int(time.time() - total_start_time) return_per_episode = utils.synthesize(logs["return_per_episode"]) rreturn_per_episode = utils.synthesize( logs["reshaped_return_per_episode"]) num_frames_per_episode = utils.synthesize( logs["num_frames_per_episode"]) header = ["update", "frames", "FPS", "duration"] data = [update, num_frames, fps, duration] header += ["rreturn_" + key for key in rreturn_per_episode.keys()] data += rreturn_per_episode.values() header += [ "num_frames_" + key for key in num_frames_per_episode.keys() ] data += num_frames_per_episode.values() header += ["entropy", "value", "policy_loss", "value_loss"] data += [ logs["entropy"], logs["value"], logs["policy_loss"], logs["value_loss"] ] header += ["grad_norm"] data += [logs["grad_norm"]] # add log fields that are not in the standard log format (for example value_int) extra_fields = extra_log_fields(header, list(logs.keys())) header.extend(extra_fields) data += [logs[field] for field in extra_fields] # print to stdout the standard log fields + fields required in config keys_format, printable_data = print_keys(header, data, extra_logs) logger.info(keys_format.format(*printable_data)) header += ["return_" + key for key in return_per_episode.keys()] data += return_per_episode.values() if status["num_frames"] == 0: csv_writer.writerow(header) csv_writer.writerow(data) csv_file.flush() if args.tb: for field, value in zip(header, data): tb_writer.add_scalar(field, value, num_frames) status = {"num_frames": num_frames, "update": update} if main_r_key is None: crt_eprew = list(rreturn_per_episode.values())[0] prev_rewards.append(crt_eprew) else: crt_eprew = logs[main_r_key] prev_rewards.append(logs[main_r_key]) # -- Save vocabulary and model if args.save_interval > 0 and update % args.save_interval == 0: preprocess_obss.vocab.save() saver.save_training_data(model, algo.get_save_data(), crt_eprew) logger.info("Model successfully saved") utils.save_status(status, model_dir) check_rew = np.mean(prev_rewards[-max_eprews_window:]) if len(prev_rewards) > max_eprews_window and check_rew > max_eprews: print( f"Reached mean return {max_eprews} for a window of {max_eprews_window} steps" ) exit()
def __init__(self, env, model_dir, model_type='PPO2', logger=None, argmax=False, use_memory=False, use_text=False, num_cpu=1, frames_per_proc=None, discount=0.99, lr=0.001, gae_lambda=0.95, entropy_coef=0.01, value_loss_coef=0.5, max_grad_norm=0.5, recurrence=1, optim_eps=1e-8, optim_alpha=None, clip_eps=0.2, epochs=4, batch_size=256): """ Initialize the Agent object. This primarily includes storing of the configuration parameters, but there is some other logic for correctly initializing the agent. :param env: the environment for training :param model_dir: the save directory (appended with the goal_id in initialization) :param model_type: the type of model {'PPO2', 'A2C'} :param logger: existing text logger :param argmax: if we use determinsitic or probabilistic action selection :param use_memory: if we are using an LSTM :param use_text: if we are using NLP to parse the goal :param num_cpu: the number of parallel instances for training :param frames_per_proc: max time_steps per process (versus constant) :param discount: the discount factor (gamma) :param lr: the learning rate :param gae_lambda: the generalized advantage estimator lambda parameter (training smoothing parameter) :param entropy_coef: relative weight for entropy loss :param value_loss_coef: relative weight for value function loss :param max_grad_norm: max scaling factor for the gradient :param recurrence: number of recurrent steps :param optim_eps: minimum value to prevent numerical instability :param optim_alpha: RMSprop decay parameter (A2C only) :param clip_eps: clipping parameter for the advantage and value function (PPO2 only) :param epochs: number of epochs in the parameter update (PPO2 only) :param batch_size: number of samples for the parameter update (PPO2 only) """ if hasattr( env, 'goal' ) and env.goal: # if the environment has a goal, set the model_dir to the goal folder self.model_dir = model_dir + env.goal.goalId + '/' else: # otherwise just use the model_dir as is self.model_dir = model_dir # store all of the input parameters self.model_type = model_type self.num_cpu = num_cpu self.frames_per_proc = frames_per_proc self.discount = discount self.lr = lr self.gae_lambda = gae_lambda self.entropy_coef = entropy_coef self.value_loss_coef = value_loss_coef self.max_grad_norm = max_grad_norm self.recurrence = recurrence self.optim_eps = optim_eps self.optim_alpha = optim_alpha self.clip_eps = clip_eps self.epochs = epochs self.batch_size = batch_size # use the existing logger and create two new ones self.txt_logger = logger self.csv_file, self.csv_logger = utils.get_csv_logger(self.model_dir) self.tb_writer = tensorboardX.SummaryWriter(self.model_dir) self.set_env( env ) # set the environment to with some additional checks and init of training_envs self.algo = None # we don't initialize the algorithm until we call init_training_algo() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.txt_logger.info(f"Device: {device}\n") try: # if we have a saved model, load it self.status = utils.get_status(self.model_dir) except OSError: # otherwise initialize the status print('error loading saved model. initializing empty model...') self.status = {"num_frames": 0, "update": 0} if self.txt_logger: self.txt_logger.info("Training status loaded\n") if "vocab" in self.status: preprocess_obss.vocab.load_vocab(self.status["vocab"]) if self.txt_logger: self.txt_logger.info("Observations preprocessor loaded") # get the obs_space and the observation pre-processor # (for manipulating gym observations into a torch-friendly format) obs_space, self.preprocess_obss = utils.get_obss_preprocessor( self.env.observation_space) self.acmodel = ACModel(obs_space, self.env.action_space, use_memory=use_memory, use_text=use_text) self.device = device # store the device {'cpu', 'cuda:N'} self.argmax = argmax # if we are using greedy action selection # or are we using probabilistic action selection if self.acmodel.recurrent: # initialize the memories self.memories = torch.zeros(num_cpu, self.acmodel.memory_size, device=self.device) if "model_state" in self.status: # if we have a saved model ('model_state') in the status # load that into the initialized model self.acmodel.load_state_dict(self.status["model_state"]) self.acmodel.to( device) # make sure the model is located on the correct device self.txt_logger.info("Model loaded\n") self.txt_logger.info("{}\n".format(self.acmodel)) # some redundant code. uncomment if there are issues and delete after enough testing #if 'model_state' in self.status: # self.acmodel.load_state_dict(self.status['model_state']) #self.acmodel.to(self.device) self.acmodel.eval() if hasattr(self.preprocess_obss, "vocab"): self.preprocess_obss.vocab.load_vocab(utils.get_vocab(model_dir))
def run(full_args: Namespace) -> None: # import torch.multiprocessing as mp # mp.set_start_method('spawn') args = full_args.main agent_args = full_args.agent model_args = full_args.model env_args = full_args.env_cfg extra_logs = getattr(full_args, "extra_logs", None) if args.seed == 0: args.seed = full_args.run_id + 1 max_eprews = args.max_eprews post_process_args(agent_args) post_process_args(model_args) model_dir = getattr(args, "model_dir", full_args.out_dir) print(model_dir) # ============================================================================================== # @ torc_rl repo original # Define logger, CSV writer and Tensorboard writer logger = utils.get_logger(model_dir) csv_file, csv_writer = utils.get_csv_writer(model_dir) tb_writer = None if args.tb: from tensorboardX import SummaryWriter tb_writer = SummaryWriter(model_dir) # Log command and all script arguments logger.info("{}\n".format(" ".join(sys.argv))) logger.info("{}\n".format(args)) # ============================================================================================== # Set seed for all randomness sources utils.seed(args.seed) # ============================================================================================== # Generate environments envs = [] # Get environment wrapper wrapper_method = getattr(full_args.env_cfg, "wrapper", None) if wrapper_method is None: def idem(x): return x env_wrapper = idem else: env_wrappers = [getattr(environment, w_p) for w_p in wrapper_method] def env_wrapp(w_env): for wrapper in env_wrappers[::-1]: w_env = wrapper(w_env) return w_env env_wrapper = env_wrapp actual_procs = getattr(args, "actual_procs", None) master_make_envs = getattr(full_args.env_cfg, "master_make_envs", False) if actual_procs: # Split envs in chunks no_envs = args.procs envs, chunk_size = get_envs(full_args, env_wrapper, no_envs, master_make=master_make_envs) first_env = envs[0][0] print( f"NO of envs / proc: {chunk_size}; No of processes {len(envs[1:])} + Master" ) else: for i in range(args.procs): env = env_wrapper(gym.make(args.env)) env.max_steps = full_args.env_cfg.max_episode_steps env.no_stacked_frames = full_args.env_cfg.no_stacked_frames env.seed(args.seed + 10000 * i) envs.append(env) first_env = envs[0] # Generate evaluation envs eval_envs = [] if full_args.env_cfg.no_eval_envs > 0: no_envs = full_args.env_cfg.no_eval_envs eval_envs, chunk_size = get_envs(full_args, env_wrapper, no_envs, master_make=master_make_envs) # Define obss preprocessor max_image_value = full_args.env_cfg.max_image_value normalize_img = full_args.env_cfg.normalize obs_space, preprocess_obss = utils.get_obss_preprocessor( args.env, first_env.observation_space, model_dir, max_image_value=max_image_value, normalize=normalize_img) # ============================================================================================== # Load training status try: status = utils.load_status(model_dir) except OSError: status = {"num_frames": 0, "update": 0} saver = utils.SaveData(model_dir, save_best=args.save_best, save_all=args.save_all) model, agent_data, other_data = None, dict(), None try: # Continue from last point model, agent_data, other_data = saver.load_training_data(best=False) logger.info("Training data exists & loaded successfully\n") except OSError: logger.info("Could not load training data\n") # ============================================================================================== # Load Model if model is None: model = get_model(model_args, obs_space, first_env.action_space, use_memory=model_args.use_memory, no_stacked_frames=env_args.no_stacked_frames) logger.info(f"Model [{model_args.name}] successfully created\n") # Print Model info logger.info("{}\n".format(model)) if torch.cuda.is_available(): model.cuda() logger.info("CUDA available: {}\n".format(torch.cuda.is_available())) # ============================================================================================== # Load Agent algo = get_agent(full_args.agent, envs, model, agent_data, preprocess_obss=preprocess_obss, reshape_reward=None, eval_envs=eval_envs) has_evaluator = hasattr(algo, "evaluate") and full_args.env_cfg.no_eval_envs > 0 # ============================================================================================== # Train model crt_eprew = 0 if "eprew" in other_data: crt_eprew = other_data["eprew"] num_frames = status["num_frames"] total_start_time = time.time() update = status["update"] update_start_time = time.time() while num_frames < args.frames: # Update model parameters logs = algo.update_parameters() num_frames += logs["num_frames"] update += 1 if has_evaluator: if update % args.eval_interval == 0: algo.evaluate() prev_start_time = update_start_time update_start_time = time.time() # Print logs if update % args.log_interval == 0: fps = logs["num_frames"] / (update_start_time - prev_start_time) duration = int(time.time() - total_start_time) return_per_episode = utils.synthesize(logs["return_per_episode"]) rreturn_per_episode = utils.synthesize( logs["reshaped_return_per_episode"]) num_frames_per_episode = utils.synthesize( logs["num_frames_per_episode"]) header = ["update", "frames", "FPS", "duration"] data = [update, num_frames, fps, duration] header += ["rreturn_" + key for key in rreturn_per_episode.keys()] data += rreturn_per_episode.values() header += [ "num_frames_" + key for key in num_frames_per_episode.keys() ] data += num_frames_per_episode.values() header += ["entropy", "value", "policy_loss", "value_loss"] data += [ logs["entropy"], logs["value"], logs["policy_loss"], logs["value_loss"] ] header += ["grad_norm"] data += [logs["grad_norm"]] # add log fields that are not in the standard log format (for example value_int) extra_fields = extra_log_fields(header, list(logs.keys())) header.extend(extra_fields) data += [logs[field] for field in extra_fields] # print to stdout the standard log fields + fields required in config keys_format, printable_data = print_keys(header, data, extra_logs) logger.info(keys_format.format(*printable_data)) header += ["return_" + key for key in return_per_episode.keys()] data += return_per_episode.values() if status["num_frames"] == 0: csv_writer.writerow(header) csv_writer.writerow(data) csv_file.flush() if args.tb: for field, value in zip(header, data): tb_writer.add_scalar(field, value, num_frames) status = {"num_frames": num_frames, "update": update} crt_eprew = list(rreturn_per_episode.values())[0] # -- Save vocabulary and model if args.save_interval > 0 and update % args.save_interval == 0: # preprocess_obss.vocab.save() saver.save_training_data(model, algo.get_save_data(), crt_eprew) logger.info("Model successfully saved") utils.save_status(status, model_dir) if crt_eprew > max_eprews != 0: print("Reached max return 0.93") exit()
# Set seed for all randomness sources utils.seed(args.seed) # Generate environments envs = [] for i in range(args.procs): env = gym.make(args.env) env.seed(args.seed + 10000*i) envs.append(env) # Define obss preprocessor obs_space, preprocess_obss = utils.get_obss_preprocessor(args.env, envs[0].observation_space, model_dir) # Load training status try: status = utils.load_status(model_dir) except OSError: status = {"num_frames": 0, "update": 0} # Define actor-critic model try: acmodel = utils.load_model(model_dir) logger.info("Model successfully loaded\n") except OSError: acmodel = ACModel(obs_space, envs[0].action_space, args.mem, args.text)
try: status = utils.get_status(model_dir + "/train") except OSError: status = {"num_frames": 0, "update": 0} txt_logger.info("Training status loaded.\n") if pretrained_model_dir is not None: try: pretrained_status = utils.get_status(pretrained_model_dir) except: txt_logger.info("Failed to load pretrained model.\n") exit(1) # Load observations preprocessor using_gnn = (args.gnn != "GRU" and args.gnn != "LSTM") obs_space, preprocess_obss = utils.get_obss_preprocessor(envs[0], using_gnn, progression_mode) if "vocab" in status and preprocess_obss.vocab is not None: preprocess_obss.vocab.load_vocab(status["vocab"]) txt_logger.info("Observations preprocessor loaded.\n") # Load model if use_mem: acmodel = RecurrentACModel(envs[0].env, obs_space, envs[0].action_space, args.ignoreLTL, args.gnn, args.dumb_ac, args.freeze_ltl) else: acmodel = ACModel(envs[0].env, obs_space, envs[0].action_space, args.ignoreLTL, args.gnn, args.dumb_ac, args.freeze_ltl) if "model_state" in status: acmodel.load_state_dict(status["model_state"]) txt_logger.info("Loading model from existing run.\n") elif args.pretrained_gnn: acmodel.load_pretrained_gnn(pretrained_status["model_state"])
experiment.add_tag(args.tag) args.experiment = experiment #experiment.log_parameter(args) # logs args TODO # Because we all like reproducibility (¯\_(ツ)_/¯) # ------------------------------------------------------------------------------ seed = 42 # np.random.randint(100) # if we want to mix up seeds np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) env = gym.make(args.env_name) env.seed(seed) #state_dim = env.observation_space.shape[0] obs_space = utils.get_obss_preprocessor(env.observation_space) #state_dim = obs_space.shape['image'] # we want a number. [NxM tiles, then # obj, color, type] total = 1 for idx, value in enumerate(obs_space['image']): total *= value state_dim = total num_actions = env.action_space.n if args.comet: print("---------------------------------------") print("Comet Experiment: %s" % (args.namestr)) print("Seed : %s" % (seed)) print("---------------------------------------") args.optimizer_parameters = {}