Reinforcement Learning: Preliminary
Okay, here’s a general guide for modeling and training Reinforcement Learning (RL) agents using PyTorch. This guide will cover the core components and steps, assuming you have a basic understanding of RL concepts (agent, environment, state, action, reward). Core RL Components in PyTorch Environment: Typically, you’ll use a library like gymnasium (the maintained fork of OpenAI Gym). Key methods: env.reset(), env.step(action), env.render(), env.close(). Key attributes: env.observation_space, env.action_space. Agent: The learning entity. It usually consists of: ...