def _save_training_info(self):
"""
Function to save the training info.
"""
# update the status dictionary
self.status = {
"num_frames": self.num_frames,
"update": self.update,
"model_state": self.acmodel.state_dict(),
"optimizer_state": self.algo.optimizer.state_dict()
}
if hasattr(self.preprocess_obss,
"vocab"): # if we are using NLP save, NLP info
self.status["vocab"] = self.preprocess_obss.vocab.vocab
utils.save_status(self.status,
self.model_dir) # save the status info to model_dir
self.txt_logger.info("Status saved")
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", "success_rate", "policy_loss", "value_loss", "grad_norm"]
data += [logs["entropy"], logs["value"], success_per_episode["mean"], logs["policy_loss"], logs["value_loss"], logs["grad_norm"]]
txt_logger.info(
"U {} | F {:06} | FPS {:04.0f} | D {} | G {} | rR:uomM {:.2f} {:.2f} {:.2f} {:.2f} | F:uomM {:.1f} {:.1f} {} {} | H {:.3f} | V {:.3f} | S {:.3f} | pL {:.3f} | vL {:.3f} | ∇ {:.3f}"
.format(*data).encode('utf-8'))
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(),
"agent_goals": algo.goals, "optimizer_state": algo.optimizer.state_dict()}
if hasattr(preprocess_obs_goals, "vocab"):
status["vocab"] = preprocess_obs_goals.vocab.vocab
utils.save_status(status, model_dir)
txt_logger.info("Status saved")
]
logger.info(
"U {} | F {:06} | FPS {:04.0f} | D {} | rR:x̄σmM {:.2f} {:.2f} {:.2f} {:.2f} | F:x̄σmM {:.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 not (status["num_frames"]):
csv_writer.writerow(header)
csv_writer.writerow(data)
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}
utils.save_status(status, save_dir)
# Save vocabulary and model
if args.save_interval > 0 and update % args.save_interval == 0:
preprocess_obss.vocab.save()
if torch.cuda.is_available():
acmodel.cpu()
utils.save_model(acmodel, save_dir)
logger.info("Model successfully saved")
if torch.cuda.is_available():
acmodel.cuda()
txt_logger.info(
"U {} | F {:06} | FPS {:04.0f} | D {} | rR:μσmM {:.2f} {:.2f} {:.2f} {:.2f} | ARPS: {:.3f} | ADR: {:.3f} | 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": algo.acmodel.state_dict(), "optimizer_state": algo.optimizer.state_dict()}
if hasattr(preprocess_obss, "vocab") and preprocess_obss.vocab is not None:
status["vocab"] = preprocess_obss.vocab.vocab
utils.save_status(status, model_dir + "/train")
txt_logger.info("Status saved")
if args.eval:
# we send the num_frames to align the eval curves with the training curves on TB
for evalu in evals:
evalu.eval(num_frames, episodes=args.eval_episodes)
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")
logs["value"], logs["policy_loss"], logs["value_loss"],
logs["grad_norm"]))
if args.tb:
writer.add_scalar("frames", num_frames, num_frames)
writer.add_scalar("FPS", fps, num_frames)
writer.add_scalar("duration", total_ellapsed_time, num_frames)
for key, value in return_per_episode.items():
writer.add_scalar("return_" + key, value, num_frames)
for key, value in rreturn_per_episode.items():
writer.add_scalar("rreturn_" + key, value, num_frames)
for key, value in num_frames_per_episode.items():
writer.add_scalar("num_frames_" + key, value, num_frames)
writer.add_scalar("entropy", logs["entropy"], num_frames)
writer.add_scalar("value", logs["value"], num_frames)
writer.add_scalar("policy_loss", logs["policy_loss"], num_frames)
writer.add_scalar("value_loss", logs["value_loss"], num_frames)
writer.add_scalar("grad_norm", logs["grad_norm"], num_frames)
# Save obss preprocessor, vocabulary and model
if args.save_interval > 0 and i % args.save_interval == 0:
obss_preprocessor.vocab.save()
if torch.cuda.is_available():
acmodel.cpu()
utils.save_model(acmodel, run_dir)
status = {"num_frames": num_frames, "i": i}
utils.save_status(status, run_dir)
logger.info("Model successfully saved")
if torch.cuda.is_available():
acmodel.cuda()
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, 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 train_i2a_model(environment_class, # name of the environment to train on (REQUIRED)
environment_model_name, # class
algorithm,
imagination_steps,
seed=1, # random seed (default: 1)
procs=16, # number of processes (default: 16)
frames=10 ** 7, # number of frames of training (default: 10e7)
log_interval=1, # number of updates between two logs (default: 1)
save_interval=10, # number of updates between two saves (default: 0, 0 means no saving)
frames_per_proc=None, # number of frames per process before update (default: 5 for A2C and 128 for PPO)
discount=0.99, # discount factor (default: 0.99)
lr=7e-4, # learning rate for optimizers (default: 7e-4)
gae_lambda=0.95, # lambda coefficient in GAE formula (default: 0.95, 1 means no gae)
entropy_coef=0.01, # entropy term coefficient (default: 0.01)
value_loss_coef=0.5, # value loss term coefficient (default: 0.5)
max_grad_norm=0.5, # maximum norm of gradient (default: 0.5)
recurrence=1, # number of steps the gradient is propagated back in time (default: 1)
optim_eps=1e-5, # Adam and RMSprop optimizer epsilon (default: 1e-5)
optim_alpha=0.99, # RMSprop optimizer apha (default: 0.99)
clip_eps=0.2, # clipping epsilon for PPO (default: 0.2)
epochs=4, # number of epochs for PPO (default: 4)
batch_size=256, # batch size for PPO (default: 256)
no_instr=False, # don't use instructions in the model
no_mem=False, # don't use memory in the model
note=None, # name suffix
tensorboard=True):
saved_arguments = locals()
date_suffix = datetime.datetime.now().strftime("%y-%m-%d-%H-%M-%S")
note = note + "_" if note else ""
model_name = "I2A-{}_{}{}_s{}_{}".format(imagination_steps, note, environment_name(environment_class), seed, date_suffix)
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)
if tensorboard:
from tensorboardX import SummaryWriter
tb_writer = SummaryWriter(model_dir)
# Log command and all script arguments
logger.info("{}\n".format(saved_arguments))
# Set seed for all randomness sources
utils.seed(seed)
# Load training status
try:
status = utils.load_status(model_dir)
except OSError:
status = {"num_frames": 0, "update": 0}
# Define actor-critic model
num_frames = status["num_frames"]
total_start_time = time.time()
update = status["update"]
environment_model = utils.load_model(utils.get_model_dir(environment_model_name))
i2a_model = I2AModel(environment_class, environment_model, imagination_steps)
algorithm.load_acmodel(i2a_model)
logger.info("Using environment model: {}\n".format(environment_model_name))
logger.info("{}\n".format(environment_model))
logger.info("Agent architecture:\n")
logger.info("{}\n".format(i2a_model))
while num_frames < frames:
# Update model parameters
update_start_time = time.time()
logs = algorithm.update_parameters()
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", "policy_loss", "value_loss", "grad_norm", "distillation_loss"]
data += [logs["entropy"], logs["value"], logs["policy_loss"], logs["value_loss"], logs["grad_norm"], logs["distillation_loss"]]
logger.info(
"U {} | F {:06} | FPS {:04.0f} | D {} | rR:x̄σmM {:.2f} {:.2f} {:.2f} {:.2f} | F:x̄σmM {:.1f} {:.1f} {} {} | H {:.3f} | V {:.3f} | pL {:.3f} | vL {:.3f} | ∇ {:.3f} | dL {:.3f}"
.format(*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 tensorboard:
for field, value in zip(header, data):
tb_writer.add_scalar(field, value, num_frames)
status = {"num_frames": num_frames, "update": update}
utils.save_status(status, model_dir)
# Save vocabulary and model
if save_interval > 0 and update % save_interval == 0:
utils.save_model(algorithm.acmodel, model_dir)
logger.info("Model successfully saved")
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()
# save to tensorboard
for field, value in zip(header, data):
tb_writer.add_scalar(field, value)
experiment.log_metric(field, value, epoch=update)
# 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, args.seed)
txt_logger.info("Status saved")
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()
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(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)
