A gated mixture of Q-functions with different discount factors, trained with undiscounted Bellman error, adapts its temporal horizon in small MiniGrid tasks, but its theoretical justification is circular and baseline comparisons are missing.
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Adaptive Multi-Horizon Reinforcement Learning
A gated mixture of Q-functions with different discount factors, trained with undiscounted Bellman error, adapts its temporal horizon in small MiniGrid tasks, but its theoretical justification is circular and baseline comparisons are missing.