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iql.py
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# source: https://github.com/gwthomas/IQL-PyTorch
# https://arxiv.org/pdf/2110.06169.pdf
import copy
import os
import random
import uuid
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import d4rl
import gym
import numpy as np
import pyrallis
import torch
import torch.nn as nn
import torch.nn.functional as F
import wandb
from torch.distributions import Normal
from torch.optim.lr_scheduler import CosineAnnealingLR
TensorBatch = List[torch.Tensor]
EXP_ADV_MAX = 100.0
LOG_STD_MIN = -20.0
LOG_STD_MAX = 2.0
@dataclass
class TrainConfig:
# Experiment
device: str = "cuda"
env: str = "halfcheetah-medium-expert-v2" # OpenAI gym environment name
seed: int = 0 # Sets Gym, PyTorch and Numpy seeds
eval_freq: int = int(5e3) # How often (time steps) we evaluate
n_episodes: int = 10 # How many episodes run during evaluation
max_timesteps: int = int(1e6) # Max time steps to run environment
checkpoints_path: Optional[str] = None # Save path
load_model: str = "" # Model load file name, "" doesn't load
# IQL
buffer_size: int = 2_000_000 # Replay buffer size
batch_size: int = 256 # Batch size for all networks
discount: float = 0.99 # Discount factor
tau: float = 0.005 # Target network update rate
beta: float = 3.0 # Inverse temperature. Small beta -> BC, big beta -> maximizing Q
iql_tau: float = 0.7 # Coefficient for asymmetric loss
iql_deterministic: bool = False # Use deterministic actor
normalize: bool = True # Normalize states
normalize_reward: bool = False # Normalize reward
vf_lr: float = 3e-4 # V function learning rate
qf_lr: float = 3e-4 # Critic learning rate
actor_lr: float = 3e-4 # Actor learning rate
actor_dropout: Optional[float] = None # Adroit uses dropout for policy network
# Wandb logging
project: str = "CORL"
group: str = "IQL-D4RL"
name: str = "IQL"
def __post_init__(self):
self.name = f"{self.name}-{self.env}-{str(uuid.uuid4())[:8]}"
if self.checkpoints_path is not None:
self.checkpoints_path = os.path.join(self.checkpoints_path, self.name)
def soft_update(target: nn.Module, source: nn.Module, tau: float):
for target_param, source_param in zip(target.parameters(), source.parameters()):
target_param.data.copy_((1 - tau) * target_param.data + tau * source_param.data)
def compute_mean_std(states: np.ndarray, eps: float) -> Tuple[np.ndarray, np.ndarray]:
mean = states.mean(0)
std = states.std(0) + eps
return mean, std
def normalize_states(states: np.ndarray, mean: np.ndarray, std: np.ndarray):
return (states - mean) / std
def wrap_env(
env: gym.Env,
state_mean: Union[np.ndarray, float] = 0.0,
state_std: Union[np.ndarray, float] = 1.0,
reward_scale: float = 1.0,
) -> gym.Env:
# PEP 8: E731 do not assign a lambda expression, use a def
def normalize_state(state):
return (
state - state_mean
) / state_std # epsilon should be already added in std.
def scale_reward(reward):
# Please be careful, here reward is multiplied by scale!
return reward_scale * reward
env = gym.wrappers.TransformObservation(env, normalize_state)
if reward_scale != 1.0:
env = gym.wrappers.TransformReward(env, scale_reward)
return env
class ReplayBuffer:
def __init__(
self,
state_dim: int,
action_dim: int,
buffer_size: int,
device: str = "cpu",
):
self._buffer_size = buffer_size
self._pointer = 0
self._size = 0
self._states = torch.zeros(
(buffer_size, state_dim), dtype=torch.float32, device=device
)
self._actions = torch.zeros(
(buffer_size, action_dim), dtype=torch.float32, device=device
)
self._rewards = torch.zeros((buffer_size, 1), dtype=torch.float32, device=device)
self._next_states = torch.zeros(
(buffer_size, state_dim), dtype=torch.float32, device=device
)
self._dones = torch.zeros((buffer_size, 1), dtype=torch.float32, device=device)
self._device = device
def _to_tensor(self, data: np.ndarray) -> torch.Tensor:
return torch.tensor(data, dtype=torch.float32, device=self._device)
# Loads data in d4rl format, i.e. from Dict[str, np.array].
def load_d4rl_dataset(self, data: Dict[str, np.ndarray]):
if self._size != 0:
raise ValueError("Trying to load data into non-empty replay buffer")
n_transitions = data["observations"].shape[0]
if n_transitions > self._buffer_size:
raise ValueError(
"Replay buffer is smaller than the dataset you are trying to load!"
)
self._states[:n_transitions] = self._to_tensor(data["observations"])
self._actions[:n_transitions] = self._to_tensor(data["actions"])
self._rewards[:n_transitions] = self._to_tensor(data["rewards"][..., None])
self._next_states[:n_transitions] = self._to_tensor(data["next_observations"])
self._dones[:n_transitions] = self._to_tensor(data["terminals"][..., None])
self._size += n_transitions
self._pointer = min(self._size, n_transitions)
print(f"Dataset size: {n_transitions}")
def sample(self, batch_size: int) -> TensorBatch:
indices = np.random.randint(0, min(self._size, self._pointer), size=batch_size)
states = self._states[indices]
actions = self._actions[indices]
rewards = self._rewards[indices]
next_states = self._next_states[indices]
dones = self._dones[indices]
return [states, actions, rewards, next_states, dones]
def add_transition(self):
# Use this method to add new data into the replay buffer during fine-tuning.
# I left it unimplemented since now we do not do fine-tuning.
raise NotImplementedError
def set_seed(
seed: int, env: Optional[gym.Env] = None, deterministic_torch: bool = False
):
if env is not None:
env.seed(seed)
env.action_space.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
torch.use_deterministic_algorithms(deterministic_torch)
def wandb_init(config: dict) -> None:
wandb.init(
config=config,
project=config["project"],
group=config["group"],
name=config["name"],
id=str(uuid.uuid4()),
)
wandb.run.save()
@torch.no_grad()
def eval_actor(
env: gym.Env, actor: nn.Module, device: str, n_episodes: int, seed: int
) -> np.ndarray:
env.seed(seed)
actor.eval()
episode_rewards = []
for _ in range(n_episodes):
state, done = env.reset(), False
episode_reward = 0.0
while not done:
action = actor.act(state, device)
state, reward, done, _ = env.step(action)
episode_reward += reward
episode_rewards.append(episode_reward)
actor.train()
return np.asarray(episode_rewards)
def return_reward_range(dataset, max_episode_steps):
returns, lengths = [], []
ep_ret, ep_len = 0.0, 0
for r, d in zip(dataset["rewards"], dataset["terminals"]):
ep_ret += float(r)
ep_len += 1
if d or ep_len == max_episode_steps:
returns.append(ep_ret)
lengths.append(ep_len)
ep_ret, ep_len = 0.0, 0
lengths.append(ep_len) # but still keep track of number of steps
assert sum(lengths) == len(dataset["rewards"])
return min(returns), max(returns)
def modify_reward(dataset, env_name, max_episode_steps=1000):
if any(s in env_name for s in ("halfcheetah", "hopper", "walker2d")):
min_ret, max_ret = return_reward_range(dataset, max_episode_steps)
dataset["rewards"] /= max_ret - min_ret
dataset["rewards"] *= max_episode_steps
elif "antmaze" in env_name:
dataset["rewards"] -= 1.0
def asymmetric_l2_loss(u: torch.Tensor, tau: float) -> torch.Tensor:
return torch.mean(torch.abs(tau - (u < 0).float()) * u**2)
class Squeeze(nn.Module):
def __init__(self, dim=-1):
super().__init__()
self.dim = dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x.squeeze(dim=self.dim)
class MLP(nn.Module):
def __init__(
self,
dims,
activation_fn: Callable[[], nn.Module] = nn.ReLU,
output_activation_fn: Callable[[], nn.Module] = None,
squeeze_output: bool = False,
dropout: Optional[float] = None,
):
super().__init__()
n_dims = len(dims)
if n_dims < 2:
raise ValueError("MLP requires at least two dims (input and output)")
layers = []
for i in range(n_dims - 2):
layers.append(nn.Linear(dims[i], dims[i + 1]))
layers.append(activation_fn())
if dropout is not None:
layers.append(nn.Dropout(dropout))
layers.append(nn.Linear(dims[-2], dims[-1]))
if output_activation_fn is not None:
layers.append(output_activation_fn())
if squeeze_output:
if dims[-1] != 1:
raise ValueError("Last dim must be 1 when squeezing")
layers.append(Squeeze(-1))
self.net = nn.Sequential(*layers)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.net(x)
class GaussianPolicy(nn.Module):
def __init__(
self,
state_dim: int,
act_dim: int,
max_action: float,
hidden_dim: int = 256,
n_hidden: int = 2,
dropout: Optional[float] = None,
):
super().__init__()
self.net = MLP(
[state_dim, *([hidden_dim] * n_hidden), act_dim],
output_activation_fn=nn.Tanh,
)
self.log_std = nn.Parameter(torch.zeros(act_dim, dtype=torch.float32))
self.max_action = max_action
def forward(self, obs: torch.Tensor) -> Normal:
mean = self.net(obs)
std = torch.exp(self.log_std.clamp(LOG_STD_MIN, LOG_STD_MAX))
return Normal(mean, std)
@torch.no_grad()
def act(self, state: np.ndarray, device: str = "cpu"):
state = torch.tensor(state.reshape(1, -1), device=device, dtype=torch.float32)
dist = self(state)
action = dist.mean if not self.training else dist.sample()
action = torch.clamp(self.max_action * action, -self.max_action, self.max_action)
return action.cpu().data.numpy().flatten()
class DeterministicPolicy(nn.Module):
def __init__(
self,
state_dim: int,
act_dim: int,
max_action: float,
hidden_dim: int = 256,
n_hidden: int = 2,
dropout: Optional[float] = None,
):
super().__init__()
self.net = MLP(
[state_dim, *([hidden_dim] * n_hidden), act_dim],
output_activation_fn=nn.Tanh,
dropout=dropout,
)
self.max_action = max_action
def forward(self, obs: torch.Tensor) -> torch.Tensor:
return self.net(obs)
@torch.no_grad()
def act(self, state: np.ndarray, device: str = "cpu"):
state = torch.tensor(state.reshape(1, -1), device=device, dtype=torch.float32)
return (
torch.clamp(self(state) * self.max_action, -self.max_action, self.max_action)
.cpu()
.data.numpy()
.flatten()
)
class TwinQ(nn.Module):
def __init__(
self, state_dim: int, action_dim: int, hidden_dim: int = 256, n_hidden: int = 2
):
super().__init__()
dims = [state_dim + action_dim, *([hidden_dim] * n_hidden), 1]
self.q1 = MLP(dims, squeeze_output=True)
self.q2 = MLP(dims, squeeze_output=True)
def both(
self, state: torch.Tensor, action: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
sa = torch.cat([state, action], 1)
return self.q1(sa), self.q2(sa)
def forward(self, state: torch.Tensor, action: torch.Tensor) -> torch.Tensor:
return torch.min(*self.both(state, action))
class ValueFunction(nn.Module):
def __init__(self, state_dim: int, hidden_dim: int = 256, n_hidden: int = 2):
super().__init__()
dims = [state_dim, *([hidden_dim] * n_hidden), 1]
self.v = MLP(dims, squeeze_output=True)
def forward(self, state: torch.Tensor) -> torch.Tensor:
return self.v(state)
class ImplicitQLearning:
def __init__(
self,
max_action: float,
actor: nn.Module,
actor_optimizer: torch.optim.Optimizer,
q_network: nn.Module,
q_optimizer: torch.optim.Optimizer,
v_network: nn.Module,
v_optimizer: torch.optim.Optimizer,
iql_tau: float = 0.7,
beta: float = 3.0,
max_steps: int = 1000000,
discount: float = 0.99,
tau: float = 0.005,
device: str = "cpu",
):
self.max_action = max_action
self.qf = q_network
self.q_target = copy.deepcopy(self.qf).requires_grad_(False).to(device)
self.vf = v_network
self.actor = actor
self.v_optimizer = v_optimizer
self.q_optimizer = q_optimizer
self.actor_optimizer = actor_optimizer
self.actor_lr_schedule = CosineAnnealingLR(self.actor_optimizer, max_steps)
self.iql_tau = iql_tau
self.beta = beta
self.discount = discount
self.tau = tau
self.total_it = 0
self.device = device
def _update_v(self, observations, actions, log_dict) -> torch.Tensor:
# Update value function
with torch.no_grad():
target_q = self.q_target(observations, actions)
v = self.vf(observations)
adv = target_q - v
v_loss = asymmetric_l2_loss(adv, self.iql_tau)
log_dict["value_loss"] = v_loss.item()
self.v_optimizer.zero_grad()
v_loss.backward()
self.v_optimizer.step()
return adv
def _update_q(
self,
next_v: torch.Tensor,
observations: torch.Tensor,
actions: torch.Tensor,
rewards: torch.Tensor,
terminals: torch.Tensor,
log_dict: Dict,
):
targets = rewards + (1.0 - terminals.float()) * self.discount * next_v.detach()
qs = self.qf.both(observations, actions)
q_loss = sum(F.mse_loss(q, targets) for q in qs) / len(qs)
log_dict["q_loss"] = q_loss.item()
self.q_optimizer.zero_grad()
q_loss.backward()
self.q_optimizer.step()
# Update target Q network
soft_update(self.q_target, self.qf, self.tau)
def _update_policy(
self,
adv: torch.Tensor,
observations: torch.Tensor,
actions: torch.Tensor,
log_dict: Dict,
):
exp_adv = torch.exp(self.beta * adv.detach()).clamp(max=EXP_ADV_MAX)
policy_out = self.actor(observations)
if isinstance(policy_out, torch.distributions.Distribution):
bc_losses = -policy_out.log_prob(actions).sum(-1, keepdim=False)
elif torch.is_tensor(policy_out):
if policy_out.shape != actions.shape:
raise RuntimeError("Actions shape missmatch")
bc_losses = torch.sum((policy_out - actions) ** 2, dim=1)
else:
raise NotImplementedError
policy_loss = torch.mean(exp_adv * bc_losses)
log_dict["actor_loss"] = policy_loss.item()
self.actor_optimizer.zero_grad()
policy_loss.backward()
self.actor_optimizer.step()
self.actor_lr_schedule.step()
def train(self, batch: TensorBatch) -> Dict[str, float]:
self.total_it += 1
(
observations,
actions,
rewards,
next_observations,
dones,
) = batch
log_dict = {}
with torch.no_grad():
next_v = self.vf(next_observations)
# Update value function
adv = self._update_v(observations, actions, log_dict)
rewards = rewards.squeeze(dim=-1)
dones = dones.squeeze(dim=-1)
# Update Q function
self._update_q(next_v, observations, actions, rewards, dones, log_dict)
# Update actor
self._update_policy(adv, observations, actions, log_dict)
return log_dict
def state_dict(self) -> Dict[str, Any]:
return {
"qf": self.qf.state_dict(),
"q_optimizer": self.q_optimizer.state_dict(),
"vf": self.vf.state_dict(),
"v_optimizer": self.v_optimizer.state_dict(),
"actor": self.actor.state_dict(),
"actor_optimizer": self.actor_optimizer.state_dict(),
"actor_lr_schedule": self.actor_lr_schedule.state_dict(),
"total_it": self.total_it,
}
def load_state_dict(self, state_dict: Dict[str, Any]):
self.qf.load_state_dict(state_dict["qf"])
self.q_optimizer.load_state_dict(state_dict["q_optimizer"])
self.q_target = copy.deepcopy(self.qf)
self.vf.load_state_dict(state_dict["vf"])
self.v_optimizer.load_state_dict(state_dict["v_optimizer"])
self.actor.load_state_dict(state_dict["actor"])
self.actor_optimizer.load_state_dict(state_dict["actor_optimizer"])
self.actor_lr_schedule.load_state_dict(state_dict["actor_lr_schedule"])
self.total_it = state_dict["total_it"]
@pyrallis.wrap()
def train(config: TrainConfig):
env = gym.make(config.env)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
dataset = d4rl.qlearning_dataset(env)
if config.normalize_reward:
modify_reward(dataset, config.env)
if config.normalize:
state_mean, state_std = compute_mean_std(dataset["observations"], eps=1e-3)
else:
state_mean, state_std = 0, 1
dataset["observations"] = normalize_states(
dataset["observations"], state_mean, state_std
)
dataset["next_observations"] = normalize_states(
dataset["next_observations"], state_mean, state_std
)
env = wrap_env(env, state_mean=state_mean, state_std=state_std)
replay_buffer = ReplayBuffer(
state_dim,
action_dim,
config.buffer_size,
config.device,
)
replay_buffer.load_d4rl_dataset(dataset)
max_action = float(env.action_space.high[0])
if config.checkpoints_path is not None:
print(f"Checkpoints path: {config.checkpoints_path}")
os.makedirs(config.checkpoints_path, exist_ok=True)
with open(os.path.join(config.checkpoints_path, "config.yaml"), "w") as f:
pyrallis.dump(config, f)
# Set seeds
seed = config.seed
set_seed(seed, env)
q_network = TwinQ(state_dim, action_dim).to(config.device)
v_network = ValueFunction(state_dim).to(config.device)
actor = (
DeterministicPolicy(
state_dim, action_dim, max_action, dropout=config.actor_dropout
)
if config.iql_deterministic
else GaussianPolicy(
state_dim, action_dim, max_action, dropout=config.actor_dropout
)
).to(config.device)
v_optimizer = torch.optim.Adam(v_network.parameters(), lr=config.vf_lr)
q_optimizer = torch.optim.Adam(q_network.parameters(), lr=config.qf_lr)
actor_optimizer = torch.optim.Adam(actor.parameters(), lr=config.actor_lr)
kwargs = {
"max_action": max_action,
"actor": actor,
"actor_optimizer": actor_optimizer,
"q_network": q_network,
"q_optimizer": q_optimizer,
"v_network": v_network,
"v_optimizer": v_optimizer,
"discount": config.discount,
"tau": config.tau,
"device": config.device,
# IQL
"beta": config.beta,
"iql_tau": config.iql_tau,
"max_steps": config.max_timesteps,
}
print("---------------------------------------")
print(f"Training IQL, Env: {config.env}, Seed: {seed}")
print("---------------------------------------")
# Initialize actor
trainer = ImplicitQLearning(**kwargs)
if config.load_model != "":
policy_file = Path(config.load_model)
trainer.load_state_dict(torch.load(policy_file))
actor = trainer.actor
wandb_init(asdict(config))
evaluations = []
for t in range(int(config.max_timesteps)):
batch = replay_buffer.sample(config.batch_size)
batch = [b.to(config.device) for b in batch]
log_dict = trainer.train(batch)
wandb.log(log_dict, step=trainer.total_it)
# Evaluate episode
if (t + 1) % config.eval_freq == 0:
print(f"Time steps: {t + 1}")
eval_scores = eval_actor(
env,
actor,
device=config.device,
n_episodes=config.n_episodes,
seed=config.seed,
)
eval_score = eval_scores.mean()
normalized_eval_score = env.get_normalized_score(eval_score) * 100.0
evaluations.append(normalized_eval_score)
print("---------------------------------------")
print(
f"Evaluation over {config.n_episodes} episodes: "
f"{eval_score:.3f} , D4RL score: {normalized_eval_score:.3f}"
)
print("---------------------------------------")
if config.checkpoints_path is not None:
torch.save(
trainer.state_dict(),
os.path.join(config.checkpoints_path, f"checkpoint_{t}.pt"),
)
wandb.log(
{"d4rl_normalized_score": normalized_eval_score}, step=trainer.total_it
)
if __name__ == "__main__":
train()