Abhishek Naik on Continuing RL & Average Reward
TalkRL: The Reinforcement Learning Podcast
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Navigating Decision-Making in Reinforcement Learning
This chapter explores the intricacies of decision-making challenges in resource allocation, particularly within reinforcement learning frameworks. Through examples like academic servers and Mars rovers, it highlights the impact of episodic versus continuing problem structures on agent behavior and learning outcomes. The discussion emphasizes the significance of temporal boundaries, algorithmic influences, and the agent's adaptability in dynamic environments.
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