This episode is about Reinforcement Learning (RL), focusing on foundational concepts and terminology.

Our sources:

https://spinningup.openai.com/en/latest/spinningup/rl_intro.html

https://gradml.mit.edu/reinforcement/value_bellman/

https://www.geeksforgeeks.org/machine-learning/bellman-equation/

We'll explain how RL agents interact with an environment by taking actions and receiving rewards to maximize cumulative return. Key ideas covered include states and observations, the different types of action spaces (discrete vs. continuous), and how an agent's behavior is dictated by a policy (deterministic or stochastic). We will also clarify important concepts like trajectories, various formulations of return, the RL problem as an optimization task, and the role of different value functions and Bellman equations in assessing states and actions. Finally, it briefly touches upon advantage functions and the formal definition of Markov Decision Processes (MDPs).

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