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✨ DQN
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Varuna Jayasiri
385
labml_nn/rl/dqn/experiment.py
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385
labml_nn/rl/dqn/experiment.py
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"""
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\(
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\def\hl1#1{{\color{orange}{#1}}}
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\def\blue#1{{\color{cyan}{#1}}}
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\def\green#1{{\color{yellowgreen}{#1}}}
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\)
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"""
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import numpy as np
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import torch
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from torch import nn
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from labml import tracker, experiment, logger, monit
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from labml_helpers.module import Module
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from labml_helpers.schedule import Piecewise
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from labml_nn.rl.dqn import QFuncLoss
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from labml_nn.rl.dqn.replay_buffer import ReplayBuffer
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from labml_nn.rl.game import Worker
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if torch.cuda.is_available():
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device = torch.device("cuda:0")
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else:
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device = torch.device("cpu")
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class Model(Module):
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"""
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## <a name="model"></a>Neural Network Model for $Q$ Values
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#### Dueling Network ⚔️
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We are using a [dueling network](https://arxiv.org/abs/1511.06581)
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to calculate Q-values.
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Intuition behind dueling network architure is that in most states
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the action doesn't matter,
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and in some states the action is significant. Dueling network allows
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this to be represented very well.
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\begin{align}
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Q^\pi(s,a) &= V^\pi(s) + A^\pi(s, a)
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\\
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\mathop{\mathbb{E}}_{a \sim \pi(s)}
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\Big[
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A^\pi(s, a)
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\Big]
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&= 0
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\end{align}
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So we create two networks for $V$ and $A$ and get $Q$ from them.
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$$
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Q(s, a) = V(s) +
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\Big(
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A(s, a) - \frac{1}{|\mathcal{A}|} \sum_{a' \in \mathcal{A}} A(s, a')
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\Big)
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$$
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We share the initial layers of the $V$ and $A$ networks.
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"""
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def __init__(self):
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"""
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### Initialize
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We need `scope` because we need multiple copies of variables
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for target network and training network.
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"""
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super().__init__()
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self.conv = nn.Sequential(
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# The first convolution layer takes a
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# 84x84 frame and produces a 20x20 frame
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nn.Conv2d(in_channels=4, out_channels=32, kernel_size=8, stride=4),
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nn.ReLU(),
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# The second convolution layer takes a
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# 20x20 frame and produces a 9x9 frame
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nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2),
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nn.ReLU(),
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# The third convolution layer takes a
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# 9x9 frame and produces a 7x7 frame
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nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1),
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nn.ReLU(),
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)
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# A fully connected layer takes the flattened
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# frame from third convolution layer, and outputs
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# 512 features
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self.lin = nn.Linear(in_features=7 * 7 * 64, out_features=512)
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self.state_score = nn.Sequential(
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nn.Linear(in_features=512, out_features=256),
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nn.ReLU(),
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nn.Linear(in_features=256, out_features=1),
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)
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self.action_score = nn.Sequential(
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nn.Linear(in_features=512, out_features=256),
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nn.ReLU(),
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nn.Linear(in_features=256, out_features=4),
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)
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#
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self.activation = nn.ReLU()
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def __call__(self, obs: torch.Tensor):
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h = self.conv(obs)
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h = h.reshape((-1, 7 * 7 * 64))
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h = self.activation(self.lin(h))
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action_score = self.action_score(h)
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state_score = self.state_score(h)
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# $Q(s, a) =V(s) + \Big(A(s, a) - \frac{1}{|\mathcal{A}|} \sum_{a' \in \mathcal{A}} A(s, a')\Big)$
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action_score_centered = action_score - action_score.mean(dim=-1, keepdim=True)
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q = state_score + action_score_centered
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return q
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def obs_to_torch(obs: np.ndarray) -> torch.Tensor:
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"""Scale observations from `[0, 255]` to `[0, 1]`"""
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return torch.tensor(obs, dtype=torch.float32, device=device) / 255.
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class Main(object):
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"""
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## <a name="main"></a>Main class
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This class runs the training loop.
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It initializes TensorFlow, handles logging and monitoring,
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and runs workers as multiple processes.
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"""
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def __init__(self):
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"""
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### Initialize
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"""
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# #### Configurations
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# number of workers
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self.n_workers = 8
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# steps sampled on each update
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self.worker_steps = 4
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# number of training iterations
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self.train_epochs = 8
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# number of updates
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self.updates = 1_000_000
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# size of mini batch for training
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self.mini_batch_size = 32
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# exploration as a function of time step
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self.exploration_coefficient = Piecewise(
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[
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(0, 1.0),
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(25_000, 0.1),
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(self.updates / 2, 0.01)
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], outside_value=0.01)
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# update target network every 250 update
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self.update_target_model = 250
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# $\beta$ for replay buffer as a function of time steps
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self.prioritized_replay_beta = Piecewise(
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[
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(0, 0.4),
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(self.updates, 1)
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], outside_value=1)
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# replay buffer
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self.replay_buffer = ReplayBuffer(2 ** 14, 0.6)
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self.model = Model().to(device)
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self.target_model = Model().to(device)
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# last observation for each worker
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# create workers
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self.workers = [Worker(47 + i) for i in range(self.n_workers)]
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# initialize tensors for observations
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self.obs = np.zeros((self.n_workers, 4, 84, 84), dtype=np.uint8)
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for worker in self.workers:
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worker.child.send(("reset", None))
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for i, worker in enumerate(self.workers):
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self.obs[i] = worker.child.recv()
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self.loss_func = QFuncLoss(0.99)
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# optimizer
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self.optimizer = torch.optim.Adam(self.model.parameters(), lr=2.5e-4)
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def _sample_action(self, q_value: torch.Tensor, exploration_coefficient: float):
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"""
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#### $\epsilon$-greedy Sampling
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When sampling actions we use a $\epsilon$-greedy strategy, where we
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take a greedy action with probabiliy $1 - \epsilon$ and
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take a random action with probability $\epsilon$.
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We refer to $\epsilon$ as *exploration*.
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"""
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with torch.no_grad():
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greedy_action = torch.argmax(q_value, dim=-1)
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random_action = torch.randint(q_value.shape[-1], greedy_action.shape, device=q_value.device)
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is_choose_rand = torch.rand(greedy_action.shape, device=q_value.device) < exploration_coefficient
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return torch.where(is_choose_rand, random_action, greedy_action).cpu().numpy()
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def sample(self, exploration_coefficient: float):
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"""### Sample data"""
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with torch.no_grad():
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# sample `SAMPLE_STEPS`
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for t in range(self.worker_steps):
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# sample actions
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q_value = self.model(obs_to_torch(self.obs))
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actions = self._sample_action(q_value, exploration_coefficient)
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# run sampled actions on each worker
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for w, worker in enumerate(self.workers):
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worker.child.send(("step", actions[w]))
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# collect information from each worker
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for w, worker in enumerate(self.workers):
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# get results after executing the actions
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next_obs, reward, done, info = worker.child.recv()
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# add transition to replay buffer
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self.replay_buffer.add(self.obs[w], actions[w], reward, next_obs, done)
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# update episode information
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# collect episode info, which is available if an episode finished;
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# this includes total reward and length of the episode -
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# look at `Game` to see how it works.
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# We also add a game frame to it for monitoring.
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if info:
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tracker.add('reward', info['reward'])
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tracker.add('length', info['length'])
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# update current observation
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self.obs[w] = next_obs
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def train(self, beta: float):
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"""
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### Train the model
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We want to find optimal action-value function.
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\begin{align}
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Q^*(s,a) &= \max_\pi \mathbb{E} \Big[
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r_t + \gamma r_{t + 1} + \gamma^2 r_{t + 2} + ... | s_t = s, a_t = a, \pi
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\Big]
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\\
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Q^*(s,a) &= \mathop{\mathbb{E}}_{s' \sim \large{\varepsilon}} \Big[
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r + \gamma \max_{a'} Q^* (s', a') | s, a
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\Big]
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\end{align}
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#### Target network 🎯
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In order to improve stability we use experience replay that randomly sample
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from previous experience $U(D)$. We also use a Q network
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with a separate set of paramters $\hl1{\theta_i^{-}}$ to calculate the target.
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$\hl1{\theta_i^{-}}$ is updated periodically.
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This is according to the [paper by DeepMind](https://deepmind.com/research/dqn/).
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So the loss function is,
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$$
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\mathcal{L}_i(\theta_i) = \mathop{\mathbb{E}}_{(s,a,r,s') \sim U(D)}
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\bigg[
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\Big(
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r + \gamma \max_{a'} Q(s', a'; \hl1{\theta_i^{-}}) - Q(s,a;\theta_i)
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\Big) ^ 2
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\bigg]
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$$
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#### Double $Q$-Learning
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The max operator in the above calculation uses same network for both
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selecting the best action and for evaluating the value.
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That is,
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$$
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\max_{a'} Q(s', a'; \theta) = \blue{Q}
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\Big(
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s', \mathop{\operatorname{argmax}}_{a'}
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\blue{Q}(s', a'; \blue{\theta}); \blue{\theta}
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\Big)
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$$
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We use [double Q-learning](https://arxiv.org/abs/1509.06461), where
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the $\operatorname{argmax}$ is taken from $\theta_i$ and
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the value is taken from $\theta_i^{-}$.
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And the loss function becomes,
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\begin{align}
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\mathcal{L}_i(\theta_i) = \mathop{\mathbb{E}}_{(s,a,r,s') \sim U(D)}
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\Bigg[
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\bigg(
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&r + \gamma \blue{Q}
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\Big(
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s',
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\mathop{\operatorname{argmax}}_{a'}
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\green{Q}(s', a'; \green{\theta_i}); \blue{\theta_i^{-}}
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\Big)
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\\
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- &Q(s,a;\theta_i)
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\bigg) ^ 2
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\Bigg]
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\end{align}
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"""
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for _ in range(self.train_epochs):
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# sample from priority replay buffer
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samples = self.replay_buffer.sample(self.mini_batch_size, beta)
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# train network
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q_value = self.model(obs_to_torch(samples['obs']))
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with torch.no_grad():
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double_q_value = self.model(obs_to_torch(samples['next_obs']))
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target_q_value = self.target_model(obs_to_torch(samples['next_obs']))
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td_errors, loss = self.loss_func(q_value,
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q_value.new_tensor(samples['action']),
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double_q_value, target_q_value,
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q_value.new_tensor(samples['done']),
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q_value.new_tensor(samples['reward']),
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q_value.new_tensor(samples['weights']))
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# $p_i = |\delta_i| + \epsilon$
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new_priorities = np.abs(td_errors.cpu().numpy()) + 1e-6
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# update replay buffer
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self.replay_buffer.update_priorities(samples['indexes'], new_priorities)
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# compute gradients
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self.optimizer.zero_grad()
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loss.backward()
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torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=0.5)
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self.optimizer.step()
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def run_training_loop(self):
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"""
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### Run training loop
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"""
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# copy to target network initially
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self.target_model.load_state_dict(self.model.state_dict())
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# last 100 episode information
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tracker.set_queue('reward', 100, True)
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tracker.set_queue('length', 100, True)
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for update in monit.loop(self.updates):
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# $\epsilon$, exploration fraction
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exploration = self.exploration_coefficient(update)
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tracker.add('exploration', exploration)
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# $\beta$ for priority replay
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beta = self.prioritized_replay_beta(update)
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tracker.add('beta', beta)
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# sample with current policy
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self.sample(exploration)
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if self.replay_buffer.is_full():
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# train the model
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self.train(beta)
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# periodically update target network
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if update % self.update_target_model == 0:
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self.target_model.load_state_dict(self.model.state_dict())
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tracker.save()
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if (update + 1) % 1_000 == 0:
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logger.log()
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def destroy(self):
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"""
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### Destroy
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Stop the workers
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"""
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for worker in self.workers:
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worker.child.send(("close", None))
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# ## Run it
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if __name__ == "__main__":
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experiment.create(name='dqn')
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m = Main()
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with experiment.start():
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m.run_training_loop()
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m.destroy()
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