Abhishek Naik on Continuing RL & Average Reward
TalkRL: The Reinforcement Learning Podcast
Evolving Average Reward Methods in Reinforcement Learning
This chapter explores the transition from discounted reward methods to innovative reward centering in reinforcement learning, detailing its impact on agent performance and learning stability. It highlights the integration of reward centering into algorithms like Q-learning and the significance of the parameter eta in average reward contexts. The discussion also clarifies misconceptions around immediate versus average rewards and examines how differential value functions contribute to these concepts.
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