def test_replay_buffer_seed():
# Assign
batch_size = 4
buffer_0 = ReplayBuffer(batch_size)
buffer_1 = ReplayBuffer(batch_size, seed=32167)
buffer_2 = ReplayBuffer(batch_size, seed=32167)
# Act
for sars in generate_sample_SARS(400, dict_type=True):
buffer_0.add(**copy.deepcopy(sars))
buffer_1.add(**copy.deepcopy(sars))
buffer_2.add(**copy.deepcopy(sars))
# Assert
for _ in range(10):
samples_0 = buffer_0.sample()
samples_1 = buffer_1.sample()
samples_2 = buffer_2.sample()
assert samples_0 != samples_1
assert samples_0 != samples_2
assert samples_1 == samples_2
def test_replay_buffer_sample():
# Assign
batch_size = 5
buffer = ReplayBuffer(batch_size=batch_size, buffer_size=10)
# Act
for (state, actions, reward, next_state, done) in generate_sample_SARS(20):
buffer.add(state=state,
action=actions,
reward=reward,
next_state=next_state,
done=done)
# Assert
samples = buffer.sample()
# (states, actions, rewards, next_states, dones)
assert len(samples["state"]) == batch_size
assert len(samples["action"]) == batch_size
assert len(samples["reward"]) == batch_size
assert len(samples["next_state"]) == batch_size
assert len(samples["done"]) == batch_size
class DDPGAgent(AgentBase):
"""
Deep Deterministic Policy Gradients (DDPG).
Instead of popular Ornstein-Uhlenbeck (OU) process for noise this agent uses Gaussian noise.
"""
name = "DDPG"
def __init__(self,
state_size: int,
action_size: int,
actor_lr: float = 2e-3,
critic_lr: float = 2e-3,
noise_scale: float = 0.2,
noise_sigma: float = 0.1,
**kwargs):
super().__init__(**kwargs)
self.device = self._register_param(kwargs, "device", DEVICE)
self.state_size = state_size
self.action_size = action_size
# Reason sequence initiation.
hidden_layers = to_numbers_seq(
self._register_param(kwargs, 'hidden_layers', (128, 128)))
self.actor = ActorBody(state_size,
action_size,
hidden_layers=hidden_layers,
gate_out=torch.tanh).to(self.device)
self.critic = CriticBody(state_size,
action_size,
hidden_layers=hidden_layers).to(self.device)
self.target_actor = ActorBody(state_size,
action_size,
hidden_layers=hidden_layers,
gate_out=torch.tanh).to(self.device)
self.target_critic = CriticBody(state_size,
action_size,
hidden_layers=hidden_layers).to(
self.device)
# Noise sequence initiation
self.noise = GaussianNoise(shape=(action_size, ),
mu=1e-8,
sigma=noise_sigma,
scale=noise_scale,
device=self.device)
# Target sequence initiation
hard_update(self.target_actor, self.actor)
hard_update(self.target_critic, self.critic)
# Optimization sequence initiation.
self.actor_lr = float(
self._register_param(kwargs, 'actor_lr', actor_lr))
self.critic_lr = float(
self._register_param(kwargs, 'critic_lr', critic_lr))
self.actor_optimizer = Adam(self.actor.parameters(), lr=self.actor_lr)
self.critic_optimizer = Adam(self.critic.parameters(),
lr=self.critic_lr)
self.max_grad_norm_actor = float(
self._register_param(kwargs, "max_grad_norm_actor", 10.0))
self.max_grad_norm_critic = float(
self._register_param(kwargs, "max_grad_norm_critic", 10.0))
self.action_min = float(self._register_param(kwargs, 'action_min', -1))
self.action_max = float(self._register_param(kwargs, 'action_max', 1))
self.action_scale = float(
self._register_param(kwargs, 'action_scale', 1))
self.gamma = float(self._register_param(kwargs, 'gamma', 0.99))
self.tau = float(self._register_param(kwargs, 'tau', 0.02))
self.batch_size = int(self._register_param(kwargs, 'batch_size', 64))
self.buffer_size = int(
self._register_param(kwargs, 'buffer_size', int(1e6)))
self.buffer = ReplayBuffer(self.batch_size, self.buffer_size)
self.warm_up = int(self._register_param(kwargs, 'warm_up', 0))
self.update_freq = int(self._register_param(kwargs, 'update_freq', 1))
self.number_updates = int(
self._register_param(kwargs, 'number_updates', 1))
# Breath, my child.
self.reset_agent()
self.iteration = 0
self._loss_actor = 0.
self._loss_critic = 0.
def reset_agent(self) -> None:
self.actor.reset_parameters()
self.critic.reset_parameters()
self.target_actor.reset_parameters()
self.target_critic.reset_parameters()
@property
def loss(self) -> Dict[str, float]:
return {'actor': self._loss_actor, 'critic': self._loss_critic}
@loss.setter
def loss(self, value):
if isinstance(value, dict):
self._loss_actor = value['actor']
self._loss_critic = value['critic']
else:
self._loss_actor = value
self._loss_critic = value
@torch.no_grad()
def act(self, obs, noise: float = 0.0) -> List[float]:
"""Acting on the observations. Returns action.
Returns:
action: (list float) Action values.
"""
obs = to_tensor(obs).float().to(self.device)
action = self.actor(obs)
action += noise * self.noise.sample()
action = torch.clamp(action * self.action_scale, self.action_min,
self.action_max)
return action.cpu().numpy().tolist()
def step(self, state, action, reward, next_state, done) -> None:
self.iteration += 1
self.buffer.add(state=state,
action=action,
reward=reward,
next_state=next_state,
done=done)
if self.iteration < self.warm_up:
return
if len(self.buffer) > self.batch_size and (self.iteration %
self.update_freq) == 0:
for _ in range(self.number_updates):
self.learn(self.buffer.sample())
def compute_value_loss(self, states, actions, next_states, rewards, dones):
next_actions = self.target_actor.act(next_states)
assert next_actions.shape == actions.shape
Q_target_next = self.target_critic.act(next_states, next_actions)
Q_target = rewards + self.gamma * Q_target_next * (1 - dones)
Q_expected = self.critic(states, actions)
assert Q_expected.shape == Q_target.shape == Q_target_next.shape
return mse_loss(Q_expected, Q_target)
def compute_policy_loss(self, states) -> None:
"""Compute Policy loss based on provided states.
Loss = Mean(-Q(s, _a) ),
where _a is actor's estimate based on state, _a = Actor(s).
"""
pred_actions = self.actor(states)
return -self.critic(states, pred_actions).mean()
def learn(self, experiences) -> None:
"""Update critics and actors"""
rewards = to_tensor(experiences['reward']).float().to(
self.device).unsqueeze(1)
dones = to_tensor(experiences['done']).type(torch.int).to(
self.device).unsqueeze(1)
states = to_tensor(experiences['state']).float().to(self.device)
actions = to_tensor(experiences['action']).to(self.device)
next_states = to_tensor(experiences['next_state']).float().to(
self.device)
assert rewards.shape == dones.shape == (self.batch_size, 1)
assert states.shape == next_states.shape == (self.batch_size,
self.state_size)
assert actions.shape == (self.batch_size, self.action_size)
# Value (critic) optimization
loss_critic = self.compute_value_loss(states, actions, next_states,
rewards, dones)
self.critic_optimizer.zero_grad()
loss_critic.backward()
nn.utils.clip_grad_norm_(self.critic.parameters(),
self.max_grad_norm_critic)
self.critic_optimizer.step()
self._loss_critic = float(loss_critic.item())
# Policy (actor) optimization
loss_actor = self.compute_policy_loss(states)
self.actor_optimizer.zero_grad()
loss_actor.backward()
nn.utils.clip_grad_norm_(self.actor.parameters(),
self.max_grad_norm_actor)
self.actor_optimizer.step()
self._loss_actor = loss_actor.item()
# Soft update target weights
soft_update(self.target_actor, self.actor, self.tau)
soft_update(self.target_critic, self.critic, self.tau)
def state_dict(self) -> Dict[str, dict]:
"""Describes agent's networks.
Returns:
state: (dict) Provides actors and critics states.
"""
return {
"actor": self.actor.state_dict(),
"target_actor": self.target_actor.state_dict(),
"critic": self.critic.state_dict(),
"target_critic": self.target_critic.state_dict()
}
def log_metrics(self,
data_logger: DataLogger,
step: int,
full_log: bool = False):
data_logger.log_value("loss/actor", self._loss_actor, step)
data_logger.log_value("loss/critic", self._loss_critic, step)
if full_log:
for idx, layer in enumerate(self.actor.layers):
if hasattr(layer, "weight"):
data_logger.create_histogram(f"actor/layer_weights_{idx}",
layer.weight, step)
if hasattr(layer, "bias") and layer.bias is not None:
data_logger.create_histogram(f"actor/layer_bias_{idx}",
layer.bias, step)
for idx, layer in enumerate(self.critic.layers):
if hasattr(layer, "weight"):
data_logger.create_histogram(f"critic/layer_weights_{idx}",
layer.weight, step)
if hasattr(layer, "bias") and layer.bias is not None:
data_logger.create_histogram(f"critic/layer_bias_{idx}",
layer.bias, step)
def get_state(self) -> AgentState:
net = dict(
actor=self.actor.state_dict(),
target_actor=self.target_actor.state_dict(),
critic=self.critic.state_dict(),
target_critic=self.target_critic.state_dict(),
)
network_state: NetworkState = NetworkState(net=net)
return AgentState(model=self.name,
state_space=self.state_size,
action_space=self.action_size,
config=self._config,
buffer=self.buffer.get_state(),
network=network_state)
def save_state(self, path: str) -> None:
agent_state = self.get_state()
torch.save(agent_state, path)
def load_state(self,
*,
path: Optional[str] = None,
agent_state: Optional[dict] = None):
if path is None and agent_state:
raise ValueError(
"Either `path` or `agent_state` must be provided to load agent's state."
)
if path is not None and agent_state is None:
agent_state = torch.load(path)
self._config = agent_state.get('config', {})
self.__dict__.update(**self._config)
self.actor.load_state_dict(agent_state['actor'])
self.critic.load_state_dict(agent_state['critic'])
self.target_actor.load_state_dict(agent_state['target_actor'])
self.target_critic.load_state_dict(agent_state['target_critic'])
class TD3Agent(AgentBase):
"""
Twin Delayed Deep Deterministic (TD3) Policy Gradient.
In short, it's a slightly modified/improved version of the DDPG. Compared to the DDPG in this package,
which uses Guassian noise, this TD3 uses Ornstein–Uhlenbeck process as the noise.
"""
name = "TD3"
def __init__(self,
state_size: int,
action_size: int,
noise_scale: float = 0.2,
noise_sigma: float = 0.1,
**kwargs):
"""
Parameters:
state_size (int): Number of input dimensions.
action_size (int): Number of output dimensions
noise_scale (float): Added noise amplitude. Default: 0.2.
noise_sigma (float): Added noise variance. Default: 0.1.
Keyword parameters:
hidden_layers (tuple of ints): Tuple defining hidden dimensions in fully connected nets. Default: (128, 128).
actor_lr (float): Learning rate for the actor (policy). Default: 0.003.
critic_lr (float): Learning rate for the critic (value function). Default: 0.003.
gamma (float): Discount value. Default: 0.99.
tau (float): Soft-copy factor. Default: 0.02.
actor_hidden_layers (tuple of ints): Shape of network for actor. Default: `hideen_layers`.
critic_hidden_layers (tuple of ints): Shape of network for critic. Default: `hideen_layers`.
max_grad_norm_actor (float) Maximum norm value for actor gradient. Default: 100.
max_grad_norm_critic (float): Maximum norm value for critic gradient. Default: 100.
batch_size (int): Number of samples used in learning. Default: 64.
buffer_size (int): Maximum number of samples to store. Default: 1e6.
warm_up (int): Number of samples to observe before starting any learning step. Default: 0.
update_freq (int): Number of steps between each learning step. Default 1.
number_updates (int): How many times to use learning step in the learning phase. Default: 1.
action_min (float): Minimum returned action value. Default: -1.
action_max (float): Maximum returned action value. Default: 1.
action_scale (float): Multipler value for action. Default: 1.
"""
super().__init__(**kwargs)
self.device = self._register_param(
kwargs, "device", DEVICE) # Default device is CUDA if available
# Reason sequence initiation.
self.state_size = state_size
self.action_size = action_size
hidden_layers = to_numbers_seq(
self._register_param(kwargs, 'hidden_layers', (128, 128)))
self.actor = ActorBody(state_size,
action_size,
hidden_layers=hidden_layers).to(self.device)
self.critic = DoubleCritic(state_size,
action_size,
CriticBody,
hidden_layers=hidden_layers).to(self.device)
self.target_actor = ActorBody(state_size,
action_size,
hidden_layers=hidden_layers).to(
self.device)
self.target_critic = DoubleCritic(state_size,
action_size,
CriticBody,
hidden_layers=hidden_layers).to(
self.device)
# Noise sequence initiation
# self.noise = GaussianNoise(shape=(action_size,), mu=1e-8, sigma=noise_sigma, scale=noise_scale, device=device)
self.noise = OUProcess(shape=action_size,
scale=noise_scale,
sigma=noise_sigma,
device=self.device)
# Target sequence initiation
hard_update(self.target_actor, self.actor)
hard_update(self.target_critic, self.critic)
# Optimization sequence initiation.
actor_lr = float(self._register_param(kwargs, 'actor_lr', 3e-3))
critic_lr = float(self._register_param(kwargs, 'critic_lr', 3e-3))
self.actor_optimizer = AdamW(self.actor.parameters(), lr=actor_lr)
self.critic_optimizer = AdamW(self.critic.parameters(), lr=critic_lr)
self.max_grad_norm_actor: float = float(
kwargs.get("max_grad_norm_actor", 100))
self.max_grad_norm_critic: float = float(
kwargs.get("max_grad_norm_critic", 100))
self.action_min = float(self._register_param(kwargs, 'action_min',
-1.))
self.action_max = float(self._register_param(kwargs, 'action_max', 1.))
self.action_scale = float(
self._register_param(kwargs, 'action_scale', 1.))
self.gamma = float(self._register_param(kwargs, 'gamma', 0.99))
self.tau = float(self._register_param(kwargs, 'tau', 0.02))
self.batch_size = int(self._register_param(kwargs, 'batch_size', 64))
self.buffer_size = int(
self._register_param(kwargs, 'buffer_size', int(1e6)))
self.buffer = ReplayBuffer(self.batch_size, self.buffer_size)
self.warm_up = int(self._register_param(kwargs, 'warm_up', 0))
self.update_freq = int(self._register_param(kwargs, 'update_freq', 1))
self.update_policy_freq = int(
self._register_param(kwargs, 'update_policy_freq', 1))
self.number_updates = int(
self._register_param(kwargs, 'number_updates', 1))
self.noise_reset_freq = int(
self._register_param(kwargs, 'noise_reset_freq', 10000))
# Breath, my child.
self.reset_agent()
self.iteration = 0
self._loss_actor = 0.
self._loss_critic = 0.
@property
def loss(self) -> Dict[str, float]:
return {'actor': self._loss_actor, 'critic': self._loss_critic}
@loss.setter
def loss(self, value):
if isinstance(value, dict):
self._loss_actor = value['actor']
self._loss_critic = value['critic']
else:
self._loss_actor = value
self._loss_critic = value
def reset_agent(self) -> None:
self.actor.reset_parameters()
self.critic.reset_parameters()
self.target_actor.reset_parameters()
self.target_critic.reset_parameters()
def act(self,
state,
epsilon: float = 0.0,
training_mode=True) -> List[float]:
"""
Agent acting on observations.
When the training_mode is True (default) a noise is added to each action.
"""
# Epsilon greedy
if self._rng.random() < epsilon:
rnd_actions = torch.rand(self.action_size) * (
self.action_max - self.action_min) - self.action_min
return rnd_actions.tolist()
with torch.no_grad():
state = to_tensor(state).float().to(self.device)
action = self.actor(state)
if training_mode:
action += self.noise.sample()
return (self.action_scale * torch.clamp(action, self.action_min,
self.action_max)).tolist()
def target_act(self, staten, noise: float = 0.0):
with torch.no_grad():
staten = to_tensor(staten).float().to(self.device)
action = self.target_actor(staten) + noise * self.noise.sample()
return torch.clamp(action, self.action_min,
self.action_max).cpu().numpy().astype(
np.float32)
def step(self, state, action, reward, next_state, done):
self.iteration += 1
self.buffer.add(state=state,
action=action,
reward=reward,
next_state=next_state,
done=done)
if (self.iteration % self.noise_reset_freq) == 0:
self.noise.reset_states()
if self.iteration < self.warm_up:
return
if len(self.buffer) <= self.batch_size:
return
if not (self.iteration % self.update_freq) or not (
self.iteration % self.update_policy_freq):
for _ in range(self.number_updates):
# Note: Inside this there's a delayed policy update.
# Every `update_policy_freq` it will learn `number_updates` times.
self.learn(self.buffer.sample())
def learn(self, experiences):
"""Update critics and actors"""
rewards = to_tensor(experiences['reward']).float().to(
self.device).unsqueeze(1)
dones = to_tensor(experiences['done']).type(torch.int).to(
self.device).unsqueeze(1)
states = to_tensor(experiences['state']).float().to(self.device)
actions = to_tensor(experiences['action']).to(self.device)
next_states = to_tensor(experiences['next_state']).float().to(
self.device)
if (self.iteration % self.update_freq) == 0:
self._update_value_function(states, actions, rewards, next_states,
dones)
if (self.iteration % self.update_policy_freq) == 0:
self._update_policy(states)
soft_update(self.target_actor, self.actor, self.tau)
soft_update(self.target_critic, self.critic, self.tau)
def _update_value_function(self, states, actions, rewards, next_states,
dones):
# critic loss
next_actions = self.target_actor.act(next_states)
Q_target_next = torch.min(
*self.target_critic.act(next_states, next_actions))
Q_target = rewards + (self.gamma * Q_target_next * (1 - dones))
Q1_expected, Q2_expected = self.critic(states, actions)
loss_critic = mse_loss(Q1_expected, Q_target) + mse_loss(
Q2_expected, Q_target)
# Minimize the loss
self.critic_optimizer.zero_grad()
loss_critic.backward()
nn.utils.clip_grad_norm_(self.critic.parameters(),
self.max_grad_norm_critic)
self.critic_optimizer.step()
self._loss_critic = float(loss_critic.item())
def _update_policy(self, states):
# Compute actor loss
pred_actions = self.actor(states)
loss_actor = -self.critic(states, pred_actions)[0].mean()
self.actor_optimizer.zero_grad()
loss_actor.backward()
nn.utils.clip_grad_norm_(self.actor.parameters(),
self.max_grad_norm_actor)
self.actor_optimizer.step()
self._loss_actor = loss_actor.item()
def state_dict(self) -> Dict[str, dict]:
"""Describes agent's networks.
Returns:
state: (dict) Provides actors and critics states.
"""
return {
"actor": self.actor.state_dict(),
"target_actor": self.target_actor.state_dict(),
"critic": self.critic.state_dict(),
"target_critic": self.target_critic()
}
def log_metrics(self,
data_logger: DataLogger,
step: int,
full_log: bool = False):
data_logger.log_value("loss/actor", self._loss_actor, step)
data_logger.log_value("loss/critic", self._loss_critic, step)
def get_state(self):
return dict(
actor=self.actor.state_dict(),
target_actor=self.target_actor.state_dict(),
critic=self.critic.state_dict(),
target_critic=self.target_critic.state_dict(),
config=self._config,
)
def save_state(self, path: str):
agent_state = self.get_state()
torch.save(agent_state, path)
def load_state(self, path: str):
agent_state = torch.load(path)
self._config = agent_state.get('config', {})
self.__dict__.update(**self._config)
self.actor.load_state_dict(agent_state['actor'])
self.critic.load_state_dict(agent_state['critic'])
self.target_actor.load_state_dict(agent_state['target_actor'])
self.target_critic.load_state_dict(agent_state['target_critic'])
class MADDPGAgent(MultiAgentType):
name = "MADDPG"
def __init__(self, state_size: int, action_size: int, num_agents: int, **kwargs):
"""Initiation of the Multi Agent DDPG.
All keywords are also passed to DDPG agents.
Parameters:
state_size (int): Dimensionality of the state.
action_size (int): Dimensionality of the action.
num_agents (int): Number of agents.
Keyword Arguments:
hidden_layers (tuple of ints): Shape for fully connected hidden layers.
noise_scale (float): Default: 1.0. Noise amplitude.
noise_sigma (float): Default: 0.5. Noise variance.
actor_lr (float): Default: 0.001. Learning rate for actor network.
critic_lr (float): Default: 0.001. Learning rate for critic network.
gamma (float): Default: 0.99. Discount value
tau (float): Default: 0.02. Soft copy value.
gradient_clip (optional float): Max norm for learning gradient. If None then no clip.
batch_size (int): Number of samples per learning.
buffer_size (int): Number of previous samples to remember.
warm_up (int): Number of samples to see before start learning.
update_freq (int): How many samples between learning sessions.
number_updates (int): How many learning cycles per learning session.
"""
self.device = self._register_param(kwargs, "device", DEVICE, update=True)
self.state_size: int = state_size
self.action_size = action_size
self.num_agents: int = num_agents
self.agent_names: List[str] = kwargs.get("agent_names", map(str, range(self.num_agents)))
hidden_layers = to_numbers_seq(self._register_param(kwargs, 'hidden_layers', (100, 100), update=True))
noise_scale = float(self._register_param(kwargs, 'noise_scale', 0.5))
noise_sigma = float(self._register_param(kwargs, 'noise_sigma', 1.0))
actor_lr = float(self._register_param(kwargs, 'actor_lr', 3e-4))
critic_lr = float(self._register_param(kwargs, 'critic_lr', 3e-4))
self.agents: Dict[str, DDPGAgent] = OrderedDict({
agent_name: DDPGAgent(
state_size, action_size,
actor_lr=actor_lr, critic_lr=critic_lr,
noise_scale=noise_scale, noise_sigma=noise_sigma,
**kwargs,
) for agent_name in self.agent_names
})
self.gamma = float(self._register_param(kwargs, 'gamma', 0.99))
self.tau = float(self._register_param(kwargs, 'tau', 0.02))
self.gradient_clip: Optional[float] = self._register_param(kwargs, 'gradient_clip')
self.batch_size = int(self._register_param(kwargs, 'batch_size', 64))
self.buffer_size = int(self._register_param(kwargs, 'buffer_size', int(1e6)))
self.buffer = ReplayBuffer(self.batch_size, self.buffer_size)
self.warm_up = int(self._register_param(kwargs, 'warm_up', 0))
self.update_freq = int(self._register_param(kwargs, 'update_freq', 1))
self.number_updates = int(self._register_param(kwargs, 'number_updates', 1))
self.critic = CriticBody(num_agents*state_size, num_agents*action_size, hidden_layers=hidden_layers).to(self.device)
self.target_critic = CriticBody(num_agents*state_size, num_agents*action_size, hidden_layers=hidden_layers).to(self.device)
self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=critic_lr)
hard_update(self.target_critic, self.critic)
self._step_data = {}
self._loss_critic: float = float('inf')
self._loss_actor: Dict[str, float] = {name: float('inf') for name in self.agent_names}
self.reset()
@property
def loss(self) -> Dict[str, float]:
out = {}
for agent_name, agent in self.agents.items():
for loss_name, loss_value in agent.loss.items():
out[f"{agent_name}_{loss_name}"] = loss_value
out[f"{agent_name}_actor"] = self._loss_actor[agent_name]
out["critic"] = self._loss_critic
return out
def reset(self):
self.iteration = 0
self.reset_agents()
def reset_agents(self):
for agent in self.agents.values():
agent.reset_agent()
self.critic.reset_parameters()
self.target_critic.reset_parameters()
def step(self, agent_name: str, state: StateType, action: ActionType, reward, next_state, done) -> None:
self._step_data[agent_name] = dict(
state=state, action=action, reward=reward, next_state=next_state, done=done,
)
def commit(self):
step_data = defaultdict(list)
for agent in self.agents:
agent_data = self._step_data[agent]
step_data['state'].append(agent_data['state'])
step_data['action'].append(agent_data['action'])
step_data['reward'].append(agent_data['reward'])
step_data['next_state'].append(agent_data['next_state'])
step_data['done'].append(agent_data['done'])
self.buffer.add(**step_data)
self._step_data = {}
self.iteration += 1
if self.iteration < self.warm_up:
return
if len(self.buffer) > self.batch_size and (self.iteration % self.update_freq) == 0:
for _ in range(self.number_updates):
samples = self.buffer.sample()
for agent_name in self.agents:
self.learn(samples, agent_name)
self.update_targets()
@torch.no_grad()
def act(self, agent_name: str, states: List[StateType], noise: float=0.0) -> List[float]:
"""Get actions from all agents. Synchronized action.
Parameters:
states: List of states per agent. Positions need to be consistent.
noise: Scale for the noise to include
Returns:
actions: List of actions that each agent wants to perform
"""
tensor_states = torch.tensor(states).reshape(-1)
agent = self.agents[agent_name]
action = agent.act(tensor_states, noise)
return action
def __flatten_actions(self, actions):
return actions.view(-1, self.num_agents*self.action_size)
def learn(self, experiences, agent_name: str) -> None:
"""update the critics and actors of all the agents """
# TODO: Just look at this mess.
agent_number = list(self.agents).index(agent_name)
agent_rewards = to_tensor(experiences['reward']).select(1, agent_number).unsqueeze(-1).float().to(self.device)
agent_dones = to_tensor(experiences['done']).select(1, agent_number).unsqueeze(-1).type(torch.int).to(self.device)
states = to_tensor(experiences['state']).to(self.device).view(self.batch_size, self.num_agents, self.state_size)
actions = to_tensor(experiences['action']).to(self.device)
next_states = to_tensor(experiences['next_state']).float().to(self.device).view(self.batch_size, self.num_agents, self.state_size)
flat_states = states.view(-1, self.num_agents*self.state_size)
flat_next_states = next_states.view(-1, self.num_agents*self.state_size)
flat_actions = actions.view(-1, self.num_agents*self.action_size)
assert agent_rewards.shape == agent_dones.shape == (self.batch_size, 1)
assert states.shape == next_states.shape == (self.batch_size, self.num_agents, self.state_size)
assert actions.shape == (self.batch_size, self.num_agents, self.action_size)
assert flat_actions.shape == (self.batch_size, self.num_agents*self.action_size)
agent = self.agents[agent_name]
next_actions = actions.detach().clone()
next_actions.data[:, agent_number] = agent.target_actor(next_states[:, agent_number, :])
assert next_actions.shape == (self.batch_size, self.num_agents, self.action_size)
# critic loss
Q_target_next = self.target_critic(flat_next_states, self.__flatten_actions(next_actions))
Q_target = agent_rewards + (self.gamma * Q_target_next * (1 - agent_dones))
Q_expected = self.critic(flat_states, flat_actions)
loss_critic = F.mse_loss(Q_expected, Q_target)
# Minimize the loss
self.critic_optimizer.zero_grad()
loss_critic.backward()
if self.gradient_clip:
nn.utils.clip_grad_norm_(self.critic.parameters(), self.gradient_clip)
self.critic_optimizer.step()
self._loss_critic = float(loss_critic.mean().item())
# Compute actor loss
pred_actions = actions.detach().clone()
# pred_actions.data[:, agent_number] = agent.actor(flat_states)
pred_actions.data[:, agent_number] = agent.actor(states[:, agent_number, :])
loss_actor = -self.critic(flat_states, self.__flatten_actions(pred_actions)).mean()
agent.actor_optimizer.zero_grad()
loss_actor.backward()
agent.actor_optimizer.step()
self._loss_actor[agent_name] = loss_actor.mean().item()
def update_targets(self):
"""soft update targets"""
for agent in self.agents.values():
soft_update(agent.target_actor, agent.actor, self.tau)
soft_update(self.target_critic, self.critic, self.tau)
def log_metrics(self, data_logger: DataLogger, step: int, full_log: bool=False):
data_logger.log_value('loss/critic', self._loss_critic, step)
for agent_name, agent in self.agents.items():
data_logger.log_values_dict(f"{agent_name}/loss", agent.loss, step)
def get_state(self) -> Dict[str, dict]:
"""Returns agents' internal states"""
agents_state = {}
agents_state['config'] = self._config
for agent_name, agent in self.agents.items():
agents_state[agent_name] = {"state": agent.state_dict(), "config": agent._config}
return agents_state
def save_state(self, path: str):
"""Saves current state of the Multi Agent instance and all related agents.
All states are stored via PyTorch's :func:`save <torch.save>` function.
Parameters:
path: (str) String path to a location where the state is store.
"""
agents_state = self.get_state()
torch.save(agents_state, path)
def load_state(self, *, path: Optional[str]=None, agent_state: Optional[dict]=None) -> None:
"""Loads the state into the Multi Agent.
The state can be provided either via path to a file that contains the state,
see :meth:`save_state <self.save_state>`, or direclty via `state`.
Parameters:
path: (str) A path where the state was saved via `save_state`.
state: (dict) Already loaded state kept in memory.
"""
if path is None and agent_state:
raise ValueError("Either `path` or `agent_state` must be provided to load agent's state.")
if path is not None and agent_state is None:
agent_state = torch.load(path)
self._config = agent_state.get('config', {})
self.__dict__.update(**self._config)
for agent_name, agent in self.agents.items():
_agent_state = agent_state[agent_name]
agent.load_state(agent_state=_agent_state["state"])
agent._config = _agent_state['config']
agent.__dict__.update(**agent._config)
def seed(self, seed: int) -> None:
for agent in self.agents.values():
agent.seed(seed)
def state_dict(self) -> Dict[str, Any]:
return {name: agent.state_dict() for (name, agent) in self.agents.items()}
class DDPGAgent(AgentBase):
"""
Deep Deterministic Policy Gradients (DDPG).
Instead of popular Ornstein-Uhlenbeck (OU) process for noise this agent uses Gaussian noise.
"""
name = "DDPG"
def __init__(self,
state_size: int,
action_size: int,
noise_scale: float = 0.2,
noise_sigma: float = 0.1,
**kwargs):
"""
Parameters:
state_size: Number of input dimensions.
action_size: Number of output dimensions
noise_scale (float): Added noise amplitude. Default: 0.2.
noise_sigma (float): Added noise variance. Default: 0.1.
Keyword parameters:
hidden_layers (tuple of ints): Tuple defining hidden dimensions in fully connected nets. Default: (64, 64).
gamma (float): Discount value. Default: 0.99.
tau (float): Soft-copy factor. Default: 0.002.
actor_lr (float): Learning rate for the actor (policy). Default: 0.0003.
critic_lr (float): Learning rate for the critic (value function). Default: 0.0003.
max_grad_norm_actor (float) Maximum norm value for actor gradient. Default: 10.
max_grad_norm_critic (float): Maximum norm value for critic gradient. Default: 10.
batch_size (int): Number of samples used in learning. Default: 64.
buffer_size (int): Maximum number of samples to store. Default: 1e6.
warm_up (int): Number of samples to observe before starting any learning step. Default: 0.
update_freq (int): Number of steps between each learning step. Default 1.
number_updates (int): How many times to use learning step in the learning phase. Default: 1.
action_min (float): Minimum returned action value. Default: -1.
action_max (float): Maximum returned action value. Default: 1.
action_scale (float): Multipler value for action. Default: 1.
"""
super().__init__(**kwargs)
self.device = self._register_param(kwargs, "device", DEVICE)
self.state_size = state_size
self.action_size = action_size
# Reason sequence initiation.
hidden_layers = to_numbers_seq(
self._register_param(kwargs, 'hidden_layers', (64, 64)))
self.actor = ActorBody(state_size,
action_size,
hidden_layers=hidden_layers,
gate_out=torch.tanh).to(self.device)
self.critic = CriticBody(state_size,
action_size,
hidden_layers=hidden_layers).to(self.device)
self.target_actor = ActorBody(state_size,
action_size,
hidden_layers=hidden_layers,
gate_out=torch.tanh).to(self.device)
self.target_critic = CriticBody(state_size,
action_size,
hidden_layers=hidden_layers).to(
self.device)
# Noise sequence initiation
self.noise = GaussianNoise(shape=(action_size, ),
mu=1e-8,
sigma=noise_sigma,
scale=noise_scale,
device=self.device)
# Target sequence initiation
hard_update(self.target_actor, self.actor)
hard_update(self.target_critic, self.critic)
# Optimization sequence initiation.
self.actor_lr = float(self._register_param(kwargs, 'actor_lr', 3e-4))
self.critic_lr = float(self._register_param(kwargs, 'critic_lr', 3e-4))
self.actor_optimizer = Adam(self.actor.parameters(), lr=self.actor_lr)
self.critic_optimizer = Adam(self.critic.parameters(),
lr=self.critic_lr)
self.max_grad_norm_actor = float(
self._register_param(kwargs, "max_grad_norm_actor", 10.0))
self.max_grad_norm_critic = float(
self._register_param(kwargs, "max_grad_norm_critic", 10.0))
self.action_min = float(self._register_param(kwargs, 'action_min', -1))
self.action_max = float(self._register_param(kwargs, 'action_max', 1))
self.action_scale = float(
self._register_param(kwargs, 'action_scale', 1))
self.gamma = float(self._register_param(kwargs, 'gamma', 0.99))
self.tau = float(self._register_param(kwargs, 'tau', 0.02))
self.batch_size = int(self._register_param(kwargs, 'batch_size', 64))
self.buffer_size = int(
self._register_param(kwargs, 'buffer_size', int(1e6)))
self.buffer = ReplayBuffer(self.batch_size, self.buffer_size)
self.warm_up = int(self._register_param(kwargs, 'warm_up', 0))
self.update_freq = int(self._register_param(kwargs, 'update_freq', 1))
self.number_updates = int(
self._register_param(kwargs, 'number_updates', 1))
# Breath, my child.
self.reset_agent()
self.iteration = 0
self._loss_actor = 0.
self._loss_critic = 0.
def reset_agent(self) -> None:
self.actor.reset_parameters()
self.critic.reset_parameters()
self.target_actor.reset_parameters()
self.target_critic.reset_parameters()
@property
def loss(self) -> Dict[str, float]:
return {'actor': self._loss_actor, 'critic': self._loss_critic}
@loss.setter
def loss(self, value):
if isinstance(value, dict):
self._loss_actor = value['actor']
self._loss_critic = value['critic']
else:
self._loss_actor = value
self._loss_critic = value
def __eq__(self, o: object) -> bool:
return super().__eq__(o) \
and self._config == o._config \
and self.buffer == o.buffer \
and self.get_network_state() == o.get_network_state()
@torch.no_grad()
def act(self, obs, noise: float = 0.0) -> List[float]:
"""Acting on the observations. Returns action.
Returns:
action: (list float) Action values.
"""
obs = to_tensor(obs).float().to(self.device)
action = self.actor(obs)
action += noise * self.noise.sample()
action = torch.clamp(action * self.action_scale, self.action_min,
self.action_max)
return action.cpu().numpy().tolist()
def step(self, state, action, reward, next_state, done) -> None:
self.iteration += 1
self.buffer.add(state=state,
action=action,
reward=reward,
next_state=next_state,
done=done)
if self.iteration < self.warm_up:
return
if len(self.buffer) > self.batch_size and (self.iteration %
self.update_freq) == 0:
for _ in range(self.number_updates):
self.learn(self.buffer.sample())
def compute_value_loss(self, states, actions, next_states, rewards, dones):
next_actions = self.target_actor.act(next_states)
assert next_actions.shape == actions.shape
Q_target_next = self.target_critic.act(next_states, next_actions)
Q_target = rewards + self.gamma * Q_target_next * (1 - dones)
Q_expected = self.critic(states, actions)
assert Q_expected.shape == Q_target.shape == Q_target_next.shape
return mse_loss(Q_expected, Q_target)
def compute_policy_loss(self, states) -> None:
"""Compute Policy loss based on provided states.
Loss = Mean(-Q(s, _a) ),
where _a is actor's estimate based on state, _a = Actor(s).
"""
pred_actions = self.actor(states)
return -self.critic(states, pred_actions).mean()
def learn(self, experiences) -> None:
"""Update critics and actors"""
rewards = to_tensor(experiences['reward']).float().to(
self.device).unsqueeze(1)
dones = to_tensor(experiences['done']).type(torch.int).to(
self.device).unsqueeze(1)
states = to_tensor(experiences['state']).float().to(self.device)
actions = to_tensor(experiences['action']).to(self.device)
next_states = to_tensor(experiences['next_state']).float().to(
self.device)
assert rewards.shape == dones.shape == (self.batch_size, 1)
assert states.shape == next_states.shape == (self.batch_size,
self.state_size)
assert actions.shape == (self.batch_size, self.action_size)
# Value (critic) optimization
loss_critic = self.compute_value_loss(states, actions, next_states,
rewards, dones)
self.critic_optimizer.zero_grad()
loss_critic.backward()
nn.utils.clip_grad_norm_(self.critic.parameters(),
self.max_grad_norm_critic)
self.critic_optimizer.step()
self._loss_critic = float(loss_critic.item())
# Policy (actor) optimization
loss_actor = self.compute_policy_loss(states)
self.actor_optimizer.zero_grad()
loss_actor.backward()
nn.utils.clip_grad_norm_(self.actor.parameters(),
self.max_grad_norm_actor)
self.actor_optimizer.step()
self._loss_actor = loss_actor.item()
# Soft update target weights
soft_update(self.target_actor, self.actor, self.tau)
soft_update(self.target_critic, self.critic, self.tau)
def state_dict(self) -> Dict[str, dict]:
"""Describes agent's networks.
Returns:
state: (dict) Provides actors and critics states.
"""
return {
"actor": self.actor.state_dict(),
"target_actor": self.target_actor.state_dict(),
"critic": self.critic.state_dict(),
"target_critic": self.target_critic.state_dict()
}
def log_metrics(self,
data_logger: DataLogger,
step: int,
full_log: bool = False):
data_logger.log_value("loss/actor", self._loss_actor, step)
data_logger.log_value("loss/critic", self._loss_critic, step)
if full_log:
for idx, layer in enumerate(self.actor.layers):
if hasattr(layer, "weight"):
data_logger.create_histogram(f"actor/layer_weights_{idx}",
layer.weight, step)
if hasattr(layer, "bias") and layer.bias is not None:
data_logger.create_histogram(f"actor/layer_bias_{idx}",
layer.bias, step)
for idx, layer in enumerate(self.critic.layers):
if hasattr(layer, "weight"):
data_logger.create_histogram(f"critic/layer_weights_{idx}",
layer.weight, step)
if hasattr(layer, "bias") and layer.bias is not None:
data_logger.create_histogram(f"critic/layer_bias_{idx}",
layer.bias, step)
def get_state(self) -> AgentState:
return AgentState(
model=self.name,
state_space=self.state_size,
action_space=self.action_size,
config=self._config,
buffer=copy.deepcopy(self.buffer.get_state()),
network=copy.deepcopy(self.get_network_state()),
)
def get_network_state(self) -> NetworkState:
net = dict(
actor=self.actor.state_dict(),
target_actor=self.target_actor.state_dict(),
critic=self.critic.state_dict(),
target_critic=self.target_critic.state_dict(),
)
return NetworkState(net=net)
@staticmethod
def from_state(state: AgentState) -> AgentBase:
config = copy.copy(state.config)
config.update({
'state_size': state.state_space,
'action_size': state.action_space
})
agent = DDPGAgent(**config)
if state.network is not None:
agent.set_network(state.network)
if state.buffer is not None:
agent.set_buffer(state.buffer)
return agent
def set_buffer(self, buffer_state: BufferState) -> None:
self.buffer = BufferFactory.from_state(buffer_state)
def set_network(self, network_state: NetworkState) -> None:
self.actor.load_state_dict(copy.deepcopy(network_state.net['actor']))
self.target_actor.load_state_dict(network_state.net['target_actor'])
self.critic.load_state_dict(network_state.net['critic'])
self.target_critic.load_state_dict(network_state.net['target_critic'])
def save_state(self, path: str) -> None:
agent_state = self.get_state()
torch.save(agent_state, path)
def load_state(self,
*,
path: Optional[str] = None,
agent_state: Optional[dict] = None):
if path is None and agent_state:
raise ValueError(
"Either `path` or `agent_state` must be provided to load agent's state."
)
if path is not None and agent_state is None:
agent_state = torch.load(path)
self._config = agent_state.get('config', {})
self.__dict__.update(**self._config)
self.actor.load_state_dict(agent_state['actor'])
self.critic.load_state_dict(agent_state['critic'])
self.target_actor.load_state_dict(agent_state['target_actor'])
self.target_critic.load_state_dict(agent_state['target_critic'])
class SACAgent(AgentType):
"""
Soft Actor-Critic.
Uses stochastic policy and dual value network (two critics).
Based on
"Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor"
by Haarnoja et al. (2018) (http://arxiv.org/abs/1801.01290).
"""
name = "SAC"
def __init__(self,
state_size: int,
action_size: int,
hidden_layers: Sequence[int] = (128, 128),
actor_lr: float = 2e-3,
critic_lr: float = 2e-3,
clip: Tuple[int, int] = (-1, 1),
alpha: float = 0.2,
device=None,
**kwargs):
self.device = device if device is not None else DEVICE
self.action_size = action_size
# Reason sequence initiation.
self.hidden_layers = kwargs.get('hidden_layers', hidden_layers)
self.policy = GaussianPolicy(action_size).to(self.device)
self.actor = ActorBody(state_size,
action_size,
hidden_layers=hidden_layers).to(self.device)
self.double_critic = DoubleCritic(state_size, action_size,
hidden_layers).to(self.device)
self.target_double_critic = DoubleCritic(state_size, action_size,
hidden_layers).to(self.device)
# Target sequence initiation
hard_update(self.target_double_critic, self.double_critic)
# Optimization sequence initiation.
self.target_entropy = -action_size
self.alpha_lr = kwargs.get("alpha_lr")
alpha_init = kwargs.get("alpha", alpha)
self.log_alpha = torch.tensor(np.log(alpha_init),
device=self.device,
requires_grad=True)
self.actor_params = list(self.actor.parameters()) + [self.policy.std]
self.critic_params = list(self.double_critic.parameters())
self.actor_optimizer = optim.Adam(self.actor_params, lr=actor_lr)
self.critic_optimizer = optim.Adam(list(self.critic_params),
lr=critic_lr)
if self.alpha_lr is not None:
self.alpha_optimizer = optim.Adam([self.log_alpha],
lr=self.alpha_lr)
self.action_min = clip[0]
self.action_max = clip[1]
self.action_scale = kwargs.get('action_scale', 1)
self.max_grad_norm_alpha: float = float(
kwargs.get("max_grad_norm_alpha", 1.0))
self.max_grad_norm_actor: float = float(
kwargs.get("max_grad_norm_actor", 20.0))
self.max_grad_norm_critic: float = float(
kwargs.get("max_grad_norm_critic", 20.0))
self.gamma: float = float(kwargs.get('gamma', 0.99))
self.tau: float = float(kwargs.get('tau', 0.02))
self.batch_size: int = int(kwargs.get('batch_size', 64))
self.buffer_size: int = int(kwargs.get('buffer_size', int(1e6)))
self.memory = Buffer(self.batch_size, self.buffer_size)
self.warm_up: int = int(kwargs.get('warm_up', 0))
self.update_freq: int = int(kwargs.get('update_freq', 1))
self.number_updates: int = int(kwargs.get('number_updates', 1))
# Breath, my child.
self.reset_agent()
self.iteration = 0
self.actor_loss = np.nan
self.critic_loss = np.nan
@property
def alpha(self):
return self.log_alpha.exp()
def reset_agent(self) -> None:
self.actor.reset_parameters()
self.double_critic.reset_parameters()
self.target_double_critic.reset_parameters()
def describe_agent(self) -> Sequence[dict]:
"""
Returns network's weights in order:
Actor, TargetActor, Critic, TargetCritic
"""
return (self.actor.state_dict(), self.double_critic.state_dict(),
self.target_double_critic.state_dict())
def act(self, state, epsilon: float = 0.0, deterministic=False):
if np.random.random() < epsilon:
return np.clip(
self.action_scale * np.random.random(size=self.action_size),
self.action_min, self.action_max)
with torch.no_grad():
state = torch.tensor(state.reshape(1, -1).astype(np.float32)).to(
self.device)
action_mu = self.actor.act(state.detach())
if deterministic:
action = action_mu
else:
action = self.policy(action_mu).sample()
action = action.cpu().numpy().flatten()
return np.clip(action * self.action_scale, self.action_min,
self.action_max)
def step(self, state, action, reward, next_state, done):
self.iteration += 1
self.memory.add(
state=state,
action=action,
reward=reward,
next_state=next_state,
done=done,
)
if self.iteration < self.warm_up:
return
if len(self.memory) > self.batch_size and (self.iteration %
self.update_freq) == 0:
for _ in range(self.number_updates):
self.learn(self.memory.sample())
def _update_value_function(self, states, actions, rewards, next_states,
dones):
# critic loss
action_mu = self.actor(next_states)
dist = self.policy(action_mu)
next_actions = dist.rsample()
# log_prob = dist.log_prob(next_actions).sum(-1, keepdim=True)
log_prob = dist.log_prob(next_actions).unsqueeze(1)
with torch.no_grad():
Q_target_next, Q2_target_next = self.double_critic.act(
next_states, next_actions)
V_target = torch.min(Q_target_next,
Q2_target_next) - self.alpha * log_prob
Q_target = rewards + self.gamma * V_target * (1 - dones)
Q_target = Q_target.type(torch.float32)
Q_expected, Q2_expected = self.double_critic(states, actions)
critic_loss = mse_loss(Q_expected, Q_target) + mse_loss(
Q2_expected, Q_target)
# Minimize the loss
self.critic_optimizer.zero_grad()
critic_loss.backward()
clip_grad_norm_(self.critic_params, self.max_grad_norm_critic)
self.critic_optimizer.step()
self.critic_loss = critic_loss.item()
def _update_policy(self, states):
# Compute actor loss
actions_mu = self.actor(states)
dist = self.policy(actions_mu)
pred_actions = dist.rsample()
log_prob = dist.log_prob(pred_actions).unsqueeze(1)
Q_actor = torch.min(*self.double_critic(states, pred_actions))
actor_loss = (self.alpha * log_prob - Q_actor).mean()
self.actor_optimizer.zero_grad()
actor_loss.backward()
clip_grad_norm_(self.actor_params, self.max_grad_norm_actor)
self.actor_optimizer.step()
self.actor_loss = actor_loss.item()
# Update alpha
if self.alpha_lr is not None:
self.alpha_optimizer.zero_grad()
alpha_loss = (self.alpha *
(-log_prob - self.target_entropy).detach()).mean()
alpha_loss.backward()
clip_grad_norm_(self.log_alpha, self.max_grad_norm_alpha)
self.alpha_optimizer.step()
def learn(self, samples):
"""update the critics and actors of all the agents """
rewards = torch.tensor(samples['reward'],
device=self.device).unsqueeze(1)
dones = torch.tensor(samples['done'],
dtype=torch.int,
device=self.device).unsqueeze(1)
states = torch.tensor(samples['state'],
dtype=torch.float32,
device=self.device)
next_states = torch.tensor(samples['next_state'],
dtype=torch.float32,
device=self.device)
actions = torch.tensor(samples['action'],
dtype=torch.float32,
device=self.device)
self._update_value_function(states, actions, rewards, next_states,
dones)
self._update_policy(states)
soft_update(self.target_double_critic, self.double_critic, self.tau)
def log_writer(self, writer, episode):
writer.add_scalar("loss/actor", self.actor_loss, episode)
writer.add_scalar("loss/critic", self.critic_loss, episode)
writer.add_scalar("loss/alpha", self.alpha, episode)
for idx, std in enumerate(self.policy.std):
writer.add_scalar(f"policy/std_{idx}", std, episode)
def save_state(self, path: str):
agent_state = dict(
actor=self.actor.state_dict(),
double_critic=self.double_critic.state_dict(),
target_double_critic=self.target_double_critic.state_dict(),
)
torch.save(agent_state, path)
def load_state(self, path: str):
agent_state = torch.load(path)
self.actor.load_state_dict(agent_state['actor'])
self.double_critic.load_state_dict(agent_state['double_critic'])
self.target_double_critic.load_state_dict(
agent_state['target_double_critic'])
class PPOAgent(AgentType):
name = "PPO"
def __init__(self, state_size: int, action_size: int, hidden_layers=(300, 200), config=None, device=None, **kwargs):
config = config if config is not None else {}
self.device = device if device is not None else DEVICE
self.state_size = state_size
self.action_size = action_size
self.iteration = 0
self.actor_lr = float(config.get('actor_lr', 3e-4))
self.critic_lr = float(config.get('critic_lr', 1e-3))
self.gamma: float = float(config.get("gamma", 0.99))
self.ppo_ratio_clip: float = float(config.get("ppo_ratio_clip", 0.2))
self.rollout_length: int = int(config.get("rollout_length", 48)) # "Much less than the episode length"
self.batch_size: int = int(config.get("batch_size", self.rollout_length // 2))
self.number_updates: int = int(config.get("number_updates", 5))
self.entropy_weight: float = float(config.get("entropy_weight", 0.0005))
self.value_loss_weight: float = float(config.get("value_loss_weight", 1.0))
self.local_memory_buffer = {}
self.memory = ReplayBuffer(batch_size=self.batch_size, buffer_size=self.rollout_length)
self.action_scale: float = float(config.get("action_scale", 1))
self.action_min: float = float(config.get("action_min", -2))
self.action_max: float = float(config.get("action_max", 2))
self.max_grad_norm_actor: float = float(config.get("max_grad_norm_actor", 100.0))
self.max_grad_norm_critic: float = float(config.get("max_grad_norm_critic", 100.0))
self.hidden_layers = config.get('hidden_layers', hidden_layers)
self.actor = ActorBody(state_size, action_size, self.hidden_layers).to(self.device)
self.critic = CriticBody(state_size, action_size, self.hidden_layers).to(self.device)
self.policy = GaussianPolicy(action_size).to(self.device)
self.actor_params = list(self.actor.parameters()) + [self.policy.std]
self.critic_params = self.critic.parameters()
self.actor_opt = torch.optim.SGD(self.actor_params, lr=self.actor_lr)
self.critic_opt = torch.optim.SGD(self.critic_params, lr=self.critic_lr)
def __clear_memory(self):
self.memory = ReplayBuffer(batch_size=self.batch_size, buffer_size=self.rollout_length)
def act(self, state, noise=0):
with torch.no_grad():
state = torch.tensor(state.reshape(1, -1).astype(np.float32)).to(self.device)
action_mu = self.actor(state)
value = self.critic(state, action_mu)
dist = self.policy(action_mu)
action = dist.sample()
logprob = dist.log_prob(action)
self.local_memory_buffer['value'] = value
self.local_memory_buffer['logprob'] = logprob
action = action.cpu().numpy().flatten()
return np.clip(action*self.action_scale, self.action_min, self.action_max)
def step(self, states, actions, rewards, next_state, done, **kwargs):
self.iteration += 1
self.memory.add(
state=states, action=actions, reward=rewards, done=done,
logprob=self.local_memory_buffer['logprob'], value=self.local_memory_buffer['value']
)
if self.iteration % self.rollout_length == 0:
self.update()
self.__clear_memory()
def ppo_iter(self, mini_batch_size, states, actions, log_probs, returns, advantage):
all_indices = np.arange(self.batch_size)
for _ in range(self.batch_size // mini_batch_size):
rand_ids = np.random.choice(all_indices, mini_batch_size, replace=False)
yield states[rand_ids], actions[rand_ids], log_probs[rand_ids], returns[rand_ids], advantage[rand_ids]
def _unpack_experiences(self, experiences):
unpacked_experiences = defaultdict(lambda: [])
for experience in experiences:
unpacked_experiences['rewards'].append(experience.reward)
unpacked_experiences['dones'].append(experience.done)
unpacked_experiences['values'].append(experience.value)
unpacked_experiences['states'].append(experience.state)
unpacked_experiences['actions'].append(experience.action)
unpacked_experiences['logprobs'].append(experience.logprob)
return unpacked_experiences
def update(self):
experiences = self.memory.sample()
rewards = torch.tensor(experiences['reward']).to(self.device)
dones = torch.tensor(experiences['done']).type(torch.int).to(self.device)
states = torch.tensor(experiences['state']).to(self.device)
actions = torch.tensor(experiences['action']).to(self.device)
values = torch.cat(experiences['value'])
log_probs = torch.cat(experiences['logprob'])
returns = revert_norm_returns(rewards, dones, self.gamma, device=self.device).unsqueeze(1)
advantages = returns - values
for _ in range(self.number_updates):
for samples in self.ppo_iter(self.batch_size, states, actions, log_probs, returns, advantages):
self.learn(samples)
def learn(self, samples):
state, action, old_log_probs, return_, advantage = samples
action_mu = self.actor(state.detach())
dist = self.policy(action_mu)
value = self.critic(state.detach(), action_mu.detach())
entropy = dist.entropy()
new_log_probs = dist.log_prob(action.detach())
r_theta = (new_log_probs - old_log_probs).exp()
r_theta_clip = torch.clamp(r_theta, 1.0 - self.ppo_ratio_clip, 1.0 + self.ppo_ratio_clip)
policy_loss = -torch.min(r_theta * advantage, r_theta_clip * advantage).mean()
entropy_loss = -self.entropy_weight * entropy.mean()
actor_loss = policy_loss + entropy_loss
self.actor_opt.zero_grad()
actor_loss.backward()
nn.utils.clip_grad_norm_(self.actor_params, self.max_grad_norm_actor)
self.actor_opt.step()
self.actor_loss = actor_loss.item()
# loss = policy_loss + value_loss + entropy_loss
value_loss = self.value_loss_weight * 0.5 * (return_ - value).pow(2).mean()
self.critic_opt.zero_grad()
value_loss.backward()
nn.utils.clip_grad_norm_(self.critic_params, self.max_grad_norm_critic)
self.critic_opt.step()
self.critic_loss = value_loss.mean().item()
def log_writer(self, writer, episode):
writer.add_scalar("loss/actor", self.actor_loss, episode)
writer.add_scalar("loss/critic", self.critic_loss, episode)
def save_state(self, path: str):
agent_state = dict(policy=self.policy.state_dict())
torch.save(agent_state, path)
def load_state(self, path: str):
agent_state = torch.load(path)
self.policy.load_state_dict(agent_state['policy'])