Introduction to Reinforcement Learning

Reinforcement learning plays a significant role in fine-tuning large language models (LLMs) through reinforcement learning from human feedback (RLHF) in AI-driven fields. Upon the completion of this course, you will be able to implement reinforcement learning and apply RL algorithms to various domains. More specifically, you will:

• Understand fundamental reinforcement learning concepts, including Markov Decision Processes, Q-learning, and policy gradients;
• Apply reinforcement learning agents using the Gymnasium library;
• Analyze the application of reinforcement learning in financial markets with the FinRL library;
• Comprehend the theory behind Proximal Policy Optimization, a key approach to Reinforcement Learning From Human Feedback (RLHF);
• Implement algorithms for classical RL problems, such as playing Atari games;
• Evaluate RL solutions effectively.