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import copy
import logging
import random
from collections import deque
from typing import List, NamedTuple, Optional, TypeVar
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import Adam
from torch.utils.tensorboard.writer import SummaryWriter
from rl_algo_impls.dqn.policy import DQNPolicy
from rl_algo_impls.shared.algorithm import Algorithm
from rl_algo_impls.shared.callbacks import Callback
from rl_algo_impls.shared.schedule import linear_schedule
from rl_algo_impls.wrappers.vectorable_wrapper import VecEnv, VecEnvObs
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, callbacks: Optional[List[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 callbacks:
if not all(
c.on_step(timesteps_elapsed=rollout_steps) for c in callbacks
):
logging.info(
f"Callback terminated training at {timesteps_elapsed} timesteps"
)
break
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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