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import copy
import numpy as np
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import deque
from torch.optim import Adam
from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs
from torch.utils.tensorboard.writer import SummaryWriter
from typing import List, NamedTuple, Optional, TypeVar
from dqn.policy import DQNPolicy
from shared.algorithm import Algorithm
from shared.callbacks.callback import Callback
from shared.schedule import linear_schedule
class Transition(NamedTuple):
obs: np.ndarray
action: np.ndarray
reward: float
done: bool
next_obs: np.ndarray
class Batch(NamedTuple):
obs: np.ndarray
actions: np.ndarray
rewards: np.ndarray
dones: np.ndarray
next_obs: np.ndarray
class ReplayBuffer:
def __init__(self, num_envs: int, maxlen: int) -> None:
self.num_envs = num_envs
self.buffer = deque(maxlen=maxlen)
def add(
self,
obs: VecEnvObs,
action: np.ndarray,
reward: np.ndarray,
done: np.ndarray,
next_obs: VecEnvObs,
) -> None:
assert isinstance(obs, np.ndarray)
assert isinstance(next_obs, np.ndarray)
for i in range(self.num_envs):
self.buffer.append(
Transition(obs[i], action[i], reward[i], done[i], next_obs[i])
)
def sample(self, batch_size: int) -> Batch:
ts = random.sample(self.buffer, batch_size)
return Batch(
obs=np.array([t.obs for t in ts]),
actions=np.array([t.action for t in ts]),
rewards=np.array([t.reward for t in ts]),
dones=np.array([t.done for t in ts]),
next_obs=np.array([t.next_obs for t in ts]),
)
def __len__(self) -> int:
return len(self.buffer)
DQNSelf = TypeVar("DQNSelf", bound="DQN")
class DQN(Algorithm):
def __init__(
self,
policy: DQNPolicy,
env: VecEnv,
device: torch.device,
tb_writer: SummaryWriter,
learning_rate: float = 1e-4,
buffer_size: int = 1_000_000,
learning_starts: int = 50_000,
batch_size: int = 32,
tau: float = 1.0,
gamma: float = 0.99,
train_freq: int = 4,
gradient_steps: int = 1,
target_update_interval: int = 10_000,
exploration_fraction: float = 0.1,
exploration_initial_eps: float = 1.0,
exploration_final_eps: float = 0.05,
max_grad_norm: float = 10.0,
) -> None:
super().__init__(policy, env, device, tb_writer)
self.policy = policy
self.optimizer = Adam(self.policy.q_net.parameters(), lr=learning_rate)
self.target_q_net = copy.deepcopy(self.policy.q_net).to(self.device)
self.target_q_net.train(False)
self.tau = tau
self.target_update_interval = target_update_interval
self.replay_buffer = ReplayBuffer(self.env.num_envs, buffer_size)
self.batch_size = batch_size
self.learning_starts = learning_starts
self.train_freq = train_freq
self.gradient_steps = gradient_steps
self.gamma = gamma
self.exploration_eps_schedule = linear_schedule(
exploration_initial_eps,
exploration_final_eps,
end_fraction=exploration_fraction,
)
self.max_grad_norm = max_grad_norm
def learn(
self: DQNSelf, total_timesteps: int, callback: Optional[Callback] = None
) -> DQNSelf:
self.policy.train(True)
obs = self.env.reset()
obs = self._collect_rollout(self.learning_starts, obs, 1)
learning_steps = total_timesteps - self.learning_starts
timesteps_elapsed = 0
steps_since_target_update = 0
while timesteps_elapsed < learning_steps:
progress = timesteps_elapsed / learning_steps
eps = self.exploration_eps_schedule(progress)
obs = self._collect_rollout(self.train_freq, obs, eps)
rollout_steps = self.train_freq
timesteps_elapsed += rollout_steps
for _ in range(
self.gradient_steps if self.gradient_steps > 0 else self.train_freq
):
self.train()
steps_since_target_update += rollout_steps
if steps_since_target_update >= self.target_update_interval:
self._update_target()
steps_since_target_update = 0
if callback:
callback.on_step(timesteps_elapsed=rollout_steps)
return self
def train(self) -> None:
if len(self.replay_buffer) < self.batch_size:
return
o, a, r, d, next_o = self.replay_buffer.sample(self.batch_size)
o = torch.as_tensor(o, device=self.device)
a = torch.as_tensor(a, device=self.device).unsqueeze(1)
r = torch.as_tensor(r, dtype=torch.float32, device=self.device)
d = torch.as_tensor(d, dtype=torch.long, device=self.device)
next_o = torch.as_tensor(next_o, device=self.device)
with torch.no_grad():
target = r + (1 - d) * self.gamma * self.target_q_net(next_o).max(1).values
current = self.policy.q_net(o).gather(dim=1, index=a).squeeze(1)
loss = F.smooth_l1_loss(current, target)
self.optimizer.zero_grad()
loss.backward()
if self.max_grad_norm:
nn.utils.clip_grad_norm_(self.policy.q_net.parameters(), self.max_grad_norm)
self.optimizer.step()
def _collect_rollout(self, timesteps: int, obs: VecEnvObs, eps: float) -> VecEnvObs:
for _ in range(0, timesteps, self.env.num_envs):
action = self.policy.act(obs, eps, deterministic=False)
next_obs, reward, done, _ = self.env.step(action)
self.replay_buffer.add(obs, action, reward, done, next_obs)
obs = next_obs
return obs
def _update_target(self) -> None:
for target_param, param in zip(
self.target_q_net.parameters(), self.policy.q_net.parameters()
):
target_param.data.copy_(
self.tau * param.data + (1 - self.tau) * target_param.data
)
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