Prospective Learning with Control proves ERM asymptotically achieves the Bayes optimal policy in non-stationary reset-free settings and outperforms time-aware RL on a 1D foraging benchmark.
Continual learning as computationally constrained reinforcement learning.Foundations and Trends® in Machine Learning, 18:913–1053
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Optimal control of the future via prospective learning with control
Prospective Learning with Control proves ERM asymptotically achieves the Bayes optimal policy in non-stationary reset-free settings and outperforms time-aware RL on a 1D foraging benchmark.