def init_training_algo(self, num_envs=None):
"""
Initialize the training algorithm.
This primarily calls the object creation functions for the A2C or PPO2 and the optimizer, but this also spawns
a number of parallel environments, based on the self.num_cpu or num_envs input (if provided).
Note, the spawning of parallel environments is VERY slow due to deepcopying the termination sets. I tried some
work arounds, but nothing worked properly, so we are stuck with it for now.
:param num_envs: an override for the default number of environments to spawn (in self.num_cpu)
"""
if not num_envs:
num_envs = self.num_cpu
if self.model_type == "A2C":
# check to make sure that the A2C parameters are set
assert self.optim_alpha
self.training_envs = [deepcopy(self.env) for i in range(num_envs)
] # spawn parallel environments
if self.acmodel.recurrent:
self.memories = torch.zeros(num_envs,
self.acmodel.memory_size,
device=self.device)
self.algo = torch_ac.A2CAlgo(
self.training_envs, self.acmodel, self.device,
self.frames_per_proc, self.discount, self.lr, self.gae_lambda,
self.entropy_coef, self.value_loss_coef, self.max_grad_norm,
self.recurrence, self.optim_alpha, self.optim_eps,
self.preprocess_obss)
elif self.model_type == "PPO2":
# check to see if the PPO2 parameters are set
assert self.clip_eps and self.epochs and self.batch_size
self.training_envs = [deepcopy(self.env) for i in range(num_envs)
] # spawn parallel environments
if self.acmodel.recurrent:
self.memories = torch.zeros(num_envs,
self.acmodel.memory_size,
device=self.device)
self.algo = torch_ac.PPOAlgo(
self.training_envs, self.acmodel, self.device,
self.frames_per_proc, self.discount, self.lr, self.gae_lambda,
self.entropy_coef, self.value_loss_coef, self.max_grad_norm,
self.recurrence, self.optim_eps, self.clip_eps, self.epochs,
self.batch_size, self.preprocess_obss)
else:
raise ValueError("Incorrect algorithm name: {}".format(algo_type))
# load the optimizer state, if it exists
if "optimizer_state" in self.status:
self.algo.optimizer.load_state_dict(self.status["optimizer_state"])
self.txt_logger.info("Optimizer loaded\n")
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"])
txt_logger.info("Pretrained model loaded.\n")
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)
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("Loading optimizer from existing run.\n")
txt_logger.info("Optimizer loaded.\n")
# init the evaluator
if args.eval:
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 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")
j = 0
if args.t_iter > 0:
teach_acmodel = ACModel(obs_space, envs[0].action_space, args.mem,
args.text)
teach_acmodel.to(device)
teacher_hist_models = [teach_acmodel]
print("Starting to teach")
if np.random.random() < args.historical_averaging:
md = copy.deepcopy(teach_acmodel)
else:
md = teach_acmodel
while j < args.t_iter:
algo_teacher = torch_ac.A2CAlgo(
[teacher_env], md, device, 10, 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)
exps, logs1 = algo_teacher.collect_experiences()
logs2 = algo_teacher.update_parameters(exps)
j += 1
if np.random.random() < args.historical_averaging:
teacher_hist_models.append(md)
md_index = np.random.choice(range(len(teacher_hist_models)), 1)[0]
md = copy.deepcopy(teacher_hist_models[md_index])
print("Finished teaching iteration ", str(j))
teacher_env.close()
print("Done teaching")
if args.nt_iters > 0:
txt_logger.info("Observations preprocessor loaded")
# Load model
acmodel = ACModel(envs[0].observation_space, envs[0].action_space, memory, False)
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 algorithm == "a2c":
algo = torch_ac.A2CAlgo(envs, acmodel, device, 5, discount, lr, gae_lambda,
entropy_coef, value_loss_coef, max_grad_norm, recurrence,
optim_alpha, optim_eps, preprocess_obss)
elif algorithm == "ppo":
algo = torch_ac.PPOAlgo(envs, acmodel, device, 128, discount, lr, gae_lambda,
entropy_coef, value_loss_coef, max_grad_norm, recurrence,
optim_eps, clip_eps, epochs, batch_size, preprocess_obss)
else:
raise ValueError("Incorrect algorithm name: {}".format(algorithm))
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"]
