OHIRL separates next-packet prediction, residual dynamics, a fixed recovery-positive evaluator, and policy learning to achieve high sign and action accuracy in reward-free perceptual tasks where standard reward proxies fail.
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Online Reward-Punishment Learning from Fixed-Channel Perceptual Event Streams without Environment Rewards
OHIRL separates next-packet prediction, residual dynamics, a fixed recovery-positive evaluator, and policy learning to achieve high sign and action accuracy in reward-free perceptual tasks where standard reward proxies fail.