Meet ONI: A Distributed Architecture for Simultaneous Reinforcement Learning Policy and Intrinsic Reward Learning with LLM Feedback

Reward functions play a crucial role in reinforcement learning (RL) systems, but their design presents significant challenges in balancing task definition simplicity with optimization effectiveness. The conventional approach of using binary rewards offers a straightforward task definition but creates optimization difficulties due to sparse learning signals. While intrinsic rewards have emerged as a solution to […]

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