class BaseAlgo(ABC):
"""The base class for RL algorithms."""
def __init__(self, envs, acmodel, device, num_frames_per_proc, discount,
lr, gae_lambda, entropy_coef, value_loss_coef, max_grad_norm,
recurrence, preprocess_obss, reshape_reward):
"""
Initializes a `BaseAlgo` instance.
Parameters:
----------
envs : list
a list of environments that will be run in parallel
acmodel : torch.Module
the model
num_frames_per_proc : int
the number of frames collected by every process for an update
discount : float
the discount for future rewards
lr : float
the learning rate for optimizers
gae_lambda : float
the lambda coefficient in the GAE formula
([Schulman et al., 2015](https://arxiv.org/abs/1506.02438))
entropy_coef : float
the weight of the entropy cost in the final objective
value_loss_coef : float
the weight of the value loss in the final objective
max_grad_norm : float
gradient will be clipped to be at most this value
recurrence : int
the number of steps the gradient is propagated back in time
preprocess_obss : function
a function that takes observations returned by the environment
and converts them into the format that the model can handle
reshape_reward : function
a function that shapes the reward, takes an
(observation, action, reward, done) tuple as an input
"""
# Store parameters
self.env = ParallelEnv(envs)
self.acmodel = acmodel
self.device = device
self.num_frames_per_proc = num_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.preprocess_obss = preprocess_obss or default_preprocess_obss
self.reshape_reward = reshape_reward
# Control parameters
assert self.acmodel.recurrent or self.recurrence == 1
assert self.num_frames_per_proc % self.recurrence == 0
# Configure acmodel
self.acmodel.to(self.device)
self.acmodel.train()
# Store helpers values
self.num_procs = len(envs)
self.num_frames = self.num_frames_per_proc * self.num_procs
# Initialize experience values
shape = (self.num_frames_per_proc, self.num_procs)
self.obs = self.env.reset()
self.obss = [None] * (shape[0])
if self.acmodel.recurrent:
self.memory = torch.zeros(shape[1],
self.acmodel.memory_size,
device=self.device)
self.memories = torch.zeros(*shape,
self.acmodel.memory_size,
device=self.device)
self.mask = torch.ones(shape[1], device=self.device)
self.masks = torch.zeros(*shape, device=self.device)
self.actions = torch.zeros(*shape, device=self.device, dtype=torch.int)
self.values = torch.zeros(*shape, device=self.device)
self.rewards = torch.zeros(*shape, device=self.device)
self.advantages = torch.zeros(*shape, device=self.device)
self.log_probs = torch.zeros(*shape, device=self.device)
# Initialize log values
self.log_episode_return = torch.zeros(self.num_procs,
device=self.device)
self.log_episode_reshaped_return = torch.zeros(self.num_procs,
device=self.device)
self.log_episode_num_frames = torch.zeros(self.num_procs,
device=self.device)
self.log_done_counter = 0
self.log_return = [0] * self.num_procs
self.log_reshaped_return = [0] * self.num_procs
self.log_num_frames = [0] * self.num_procs
def collect_experiences(self):
"""Collects rollouts and computes advantages.
Runs several environments concurrently. The next actions are computed
in a batch mode for all environments at the same time. The rollouts
and advantages from all environments are concatenated together.
Returns
-------
exps : DictList
Contains actions, rewards, advantages etc as attributes.
Each attribute, e.g. `exps.reward` has a shape
(self.num_frames_per_proc * num_envs, ...). k-th block
of consecutive `self.num_frames_per_proc` frames contains
data obtained from the k-th environment. Be careful not to mix
data from different environments!
logs : dict
Useful stats about the training process, including the average
reward, policy loss, value loss, etc.
"""
for i in range(self.num_frames_per_proc):
# Do one agent-environment interaction
preprocessed_obs = self.preprocess_obss(self.obs,
device=self.device)
with torch.no_grad():
if self.acmodel.recurrent:
dist, value, memory = self.acmodel(
preprocessed_obs, self.memory * self.mask.unsqueeze(1))
else:
dist, value = self.acmodel(preprocessed_obs)
action = dist.sample()
obs, reward, done, _ = self.env.step(action.cpu().numpy())
# Update experiences values
self.obss[i] = self.obs
self.obs = obs
if self.acmodel.recurrent:
self.memories[i] = self.memory
self.memory = memory
self.masks[i] = self.mask
self.mask = 1 - torch.tensor(
done, device=self.device, dtype=torch.float)
self.actions[i] = action
self.values[i] = value
if self.reshape_reward is not None:
self.rewards[i] = torch.tensor([
self.reshape_reward(obs_, action_, reward_, done_)
for obs_, action_, reward_, done_ in zip(
obs, action, reward, done)
],
device=self.device)
else:
self.rewards[i] = torch.tensor(reward, device=self.device)
self.log_probs[i] = dist.log_prob(action)
# Update log values
self.log_episode_return += torch.tensor(reward,
device=self.device,
dtype=torch.float)
self.log_episode_reshaped_return += self.rewards[i]
self.log_episode_num_frames += torch.ones(self.num_procs,
device=self.device)
for i, done_ in enumerate(done):
if done_:
self.log_done_counter += 1
self.log_return.append(self.log_episode_return[i].item())
self.log_reshaped_return.append(
self.log_episode_reshaped_return[i].item())
self.log_num_frames.append(
self.log_episode_num_frames[i].item())
self.log_episode_return *= self.mask
self.log_episode_reshaped_return *= self.mask
self.log_episode_num_frames *= self.mask
# Add advantage and return to experiences
preprocessed_obs = self.preprocess_obss(self.obs, device=self.device)
with torch.no_grad():
if self.acmodel.recurrent:
_, next_value, _ = self.acmodel(
preprocessed_obs, self.memory * self.mask.unsqueeze(1))
else:
_, next_value = self.acmodel(preprocessed_obs)
for i in reversed(range(self.num_frames_per_proc)):
next_mask = self.masks[
i + 1] if i < self.num_frames_per_proc - 1 else self.mask
next_value = self.values[
i + 1] if i < self.num_frames_per_proc - 1 else next_value
next_advantage = self.advantages[
i + 1] if i < self.num_frames_per_proc - 1 else 0
delta = self.rewards[
i] + self.discount * next_value * next_mask - self.values[i]
self.advantages[
i] = delta + self.discount * self.gae_lambda * next_advantage * next_mask
# Define experiences:
# the whole experience is the concatenation of the experience
# of each process.
# In comments below:
# - T is self.num_frames_per_proc,
# - P is self.num_procs,
# - D is the dimensionality.
exps = DictList()
exps.obs = [
self.obss[i][j] for j in range(self.num_procs)
for i in range(self.num_frames_per_proc)
]
if self.acmodel.recurrent:
# T x P x D -> P x T x D -> (P * T) x D
exps.memory = self.memories.transpose(0, 1).reshape(
-1, *self.memories.shape[2:])
# T x P -> P x T -> (P * T) x 1
exps.mask = self.masks.transpose(0, 1).reshape(-1).unsqueeze(1)
# for all tensors below, T x P -> P x T -> P * T
exps.action = self.actions.transpose(0, 1).reshape(-1)
exps.value = self.values.transpose(0, 1).reshape(-1)
exps.reward = self.rewards.transpose(0, 1).reshape(-1)
exps.advantage = self.advantages.transpose(0, 1).reshape(-1)
exps.returnn = exps.value + exps.advantage
exps.log_prob = self.log_probs.transpose(0, 1).reshape(-1)
# Preprocess experiences
exps.obs = self.preprocess_obss(exps.obs, device=self.device)
# Log some values
keep = max(self.log_done_counter, self.num_procs)
logs = {
"return_per_episode":
self.log_return[-keep:],
"reshaped_return_per_episode":
self.log_reshaped_return[-keep:],
"num_frames_per_episode":
self.log_num_frames[-keep:],
"num_frames":
self.num_frames,
"debug_last_env_reward":
sum(exps.reward[-self.num_frames_per_proc:]).item()
}
self.log_done_counter = 0
self.log_return = self.log_return[-self.num_procs:]
self.log_reshaped_return = self.log_reshaped_return[-self.num_procs:]
self.log_num_frames = self.log_num_frames[-self.num_procs:]
return exps, logs
@abstractmethod
def update_parameters(self):
pass
# Set device
device = torch.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(args.procs):
envs.append(utils.make_env(args.env, args.seed + 10000 * i))
eval_envs = []
for i in range(args.procs):
eval_env_name = args.eval_env if args.eval_env else args.env
eval_envs.append(utils.make_env(eval_env_name, args.seed + 10000 * i))
parallel_eval_env = ParallelEnv(eval_envs) # do this here cause we change the attribute directly on algo (instead of re-instantiating)
parallel_env = ParallelEnv(envs)
txt_logger.info("Environments loaded\n")
# Load training status
try:
if args.load_status:
status = utils.get_status(model_dir)
else:
status = {"num_frames": 0, "update": 0}
except OSError:
status = {"num_frames": 0, "update": 0}
txt_logger.info("Training status loaded\n")
def __init__(self, envs, acmodel, device, num_frames_per_proc, discount,
lr, gae_lambda, entropy_coef, value_loss_coef, max_grad_norm,
recurrence, preprocess_obss, reshape_reward):
"""
Initializes a `BaseAlgo` instance.
Parameters:
----------
envs : list
a list of environments that will be run in parallel
acmodel : torch.Module
the model
num_frames_per_proc : int
the number of frames collected by every process for an update
discount : float
the discount for future rewards
lr : float
the learning rate for optimizers
gae_lambda : float
the lambda coefficient in the GAE formula
([Schulman et al., 2015](https://arxiv.org/abs/1506.02438))
entropy_coef : float
the weight of the entropy cost in the final objective
value_loss_coef : float
the weight of the value loss in the final objective
max_grad_norm : float
gradient will be clipped to be at most this value
recurrence : int
the number of steps the gradient is propagated back in time
preprocess_obss : function
a function that takes observations returned by the environment
and converts them into the format that the model can handle
reshape_reward : function
a function that shapes the reward, takes an
(observation, action, reward, done) tuple as an input
"""
# Store parameters
self.env = ParallelEnv(envs)
self.acmodel = acmodel
self.device = device
self.num_frames_per_proc = num_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.preprocess_obss = preprocess_obss or default_preprocess_obss
self.reshape_reward = reshape_reward
# Control parameters
assert self.acmodel.recurrent or self.recurrence == 1
assert self.num_frames_per_proc % self.recurrence == 0
# Configure acmodel
self.acmodel.to(self.device)
self.acmodel.train()
# Store helpers values
self.num_procs = len(envs)
self.num_frames = self.num_frames_per_proc * self.num_procs
# Initialize experience values
shape = (self.num_frames_per_proc, self.num_procs)
self.obs = self.env.reset()
self.obss = [None] * (shape[0])
if self.acmodel.recurrent:
self.memory = torch.zeros(shape[1],
self.acmodel.memory_size,
device=self.device)
self.memories = torch.zeros(*shape,
self.acmodel.memory_size,
device=self.device)
self.mask = torch.ones(shape[1], device=self.device)
self.masks = torch.zeros(*shape, device=self.device)
self.actions = torch.zeros(*shape, device=self.device, dtype=torch.int)
self.values = torch.zeros(*shape, device=self.device)
self.rewards = torch.zeros(*shape, device=self.device)
self.advantages = torch.zeros(*shape, device=self.device)
self.log_probs = torch.zeros(*shape, device=self.device)
# Initialize log values
self.log_episode_return = torch.zeros(self.num_procs,
device=self.device)
self.log_episode_reshaped_return = torch.zeros(self.num_procs,
device=self.device)
self.log_episode_num_frames = torch.zeros(self.num_procs,
device=self.device)
self.log_done_counter = 0
self.log_return = [0] * self.num_procs
self.log_reshaped_return = [0] * self.num_procs
self.log_num_frames = [0] * self.num_procs
