A behavior-constrained RL framework with receding-horizon credit assignment learns high-performance control policies that stay aligned with expert behavior in race car simulation.
Understanding Self-Predictive Learn- ing for Reinforcement Learning
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BYOL-γ uses self-predictive representations to approximate successor representations, improving zero-shot combinatorial generalization in goal-conditioned behavioral cloning.
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Behavior-Constrained Reinforcement Learning with Receding-Horizon Credit Assignment for High-Performance Control
A behavior-constrained RL framework with receding-horizon credit assignment learns high-performance control policies that stay aligned with expert behavior in race car simulation.
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Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning
BYOL-γ uses self-predictive representations to approximate successor representations, improving zero-shot combinatorial generalization in goal-conditioned behavioral cloning.