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108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
"""
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---
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title: Deep Q Network (DQN) Model
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summary: Implementation of neural network model for Deep Q Network (DQN).
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---
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# Deep Q Network (DQN) Model
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[](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/rl/dqn/experiment.ipynb)
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[](https://app.labml.ai/run/a0da8048235511ecb9affd797fa27714)
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"""
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import torch
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from torch import nn
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from labml_helpers.module import Module
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class Model(Module):
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"""
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## Dueling Network ⚔️ Model for $Q$ Values
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We are using a [dueling network](https://papers.labml.ai/paper/1511.06581)
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to calculate Q-values.
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Intuition behind dueling network architecture 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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super().__init__()
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self.conv = nn.Sequential(
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# The first convolution layer takes a
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# $84\times84$ frame and produces a $20\times20$ 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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# $20\times20$ frame and produces a $9\times9$ 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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# $9\times9$ frame and produces a $7\times7$ 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.activation = nn.ReLU()
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# This head gives the state value $V$
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self.state_value = 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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# This head gives the action value $A$
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self.action_value = 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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def forward(self, obs: torch.Tensor):
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# Convolution
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h = self.conv(obs)
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# Reshape for linear layers
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h = h.reshape((-1, 7 * 7 * 64))
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# Linear layer
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h = self.activation(self.lin(h))
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# $A$
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action_value = self.action_value(h)
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# $V$
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state_value = self.state_value(h)
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# $A(s, a) - \frac{1}{|\mathcal{A}|} \sum_{a' \in \mathcal{A}} A(s, a')$
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action_score_centered = action_value - action_value.mean(dim=-1, keepdim=True)
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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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q = state_value + action_score_centered
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return q
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