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.
URL http://arxiv.org/abs/ 1402.0590
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Proposes an exploratory diagnostic workflow to highlight behavioral variation along MORL Pareto fronts not captured by objective values, with validation on grid and continuous control tasks.
citing papers explorer
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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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Objective-Behavior Alignment: Diagnostics for MORL Policy Selection
Proposes an exploratory diagnostic workflow to highlight behavioral variation along MORL Pareto fronts not captured by objective values, with validation on grid and continuous control tasks.