Deep-Q learning with pytorch
Welcome to the torchagent repository. This repository contains the sources for the torchagent library.
What is it?
torchagent is a library that implements various reinforcement learning algorithms for PyTorch.
You can use this library in combination with openAI Gym to implement reinforcement learning solutions.
Which algorithms are included?
Currently the following algorithms are implemented:
Deep Q Learning
Double Q Learning
You can install the library using the following command:
pip install torchagent
The following code shows a basic agent that uses Deep Q Learning.
from torchagent.memory import SequentialMemory from torchagent.agents import DQNAgent import torch import torch.nn as nn import torch.optim as optim class PolicyNetwork(nn.Module): def __init__(self): self.linear = nn.Linear(210 * 160, 3) def forward(self, x): return self.linear(x) policy_network = PolicyNetwork() memory = SequentialMemory(20) agent = DQNAgent(2, policy_network, nn.MSELoss(), optim.Adam(policy_network.parameters()), memory) env = gym.make('Assault-v0') for _ in range(50): state = env.reset() for t in count(): action = agent.act(state) next_state, reward, done, _ = env.step(agent.act(state)) agent.record(state, action, next_state, reward, done) agent.train() state = next_state if done: break
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