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cs.LG2024
Satisficing Exploration for Deep Reinforcement Learning
Dilip Arumugam, Saurabh Kumar, Ramki Gummadi +1
A default assumption in the design of reinforcement-learning algorithms is that a decision-making agent always explores to learn optimal behavior. In sufficiently complex environme…
cs.LG2024
Exploration Unbound
Dilip Arumugam, Wanqiao Xu, Benjamin Van Roy
A sequential decision-making agent balances between exploring to gain new knowledge about an environment and exploiting current knowledge to maximize immediate reward. For environm…