Chapter 13: Reinforcement Learning with PyTorch
Abstract: Reinforcement Learning (RL) with PyTorch involves leveraging PyTorch's capabilities to build and train agents that learn to make optimal decisions in an environment through trial and error. This process typically involves the following key components and steps: 1. Environment Interaction: An agent interacts with an environment, observing its state and taking actions. The environment, in response, provides a new state and a reward signal, indicating the quality of the action. Popular environments for RL are often provided by libraries like OpenAI Gym or specific simulators like VMAS for multi-agent scenarios. 2. Agent Design with PyTorch: Policy Network: A neural network, often implemented using torch.nn.Module , that takes the current state as input and outputs a probability distribution over possible actions (for policy-based methods like PPO or REINFORCE) or Q-values for each action (for value-based methods like DQN). Value Network (Optional): ...