Short answers, pulled from the story.
Reinforcement learning defines an agent as any entity that takes actions within a dynamic environment to maximize a reward signal. Unlike supervised learning which relies on labeled data, this field trains agents through direct interaction with their surroundings.
Arthur Samuel wrote about machine learning that could improve through experience rather than explicit programming in 1956. This early concept laid the groundwork for what would become reinforcement learning decades later.
Epsilon serves as a parameter controlling how much exploration versus exploitation occurs where one minus epsilon probability selects the best action. When ties occur between actions they break uniformly at random while epsilon probability chooses an action uniformly at random from all possibilities.
Google DeepMind increased attention to deep reinforcement learning through work on learning ATARI games without explicitly designing state spaces. The technique initially appeared in development of InstructGPT before appearing later in ChatGPT which incorporates RLHF for improving output responses ensuring safety measures.
Continuous or high-dimensional action spaces make learning steps more complex less predictable compared to discrete environments. Policy search methods get stuck in local optima frequently because they rely on local search strategies causing instability prone to divergence from small changes in policies.