The paper introduces a building-control benchmark with formalized environment variations and shows that a state-of-the-art multi-objective RL method generalizes unevenly across dynamics and climate contexts.
Learning action representations for reinforcement learning
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BEAVER: Building Environments with Assessable Variation for Evaluating Multi-Objective Reinforcement Learning
The paper introduces a building-control benchmark with formalized environment variations and shows that a state-of-the-art multi-objective RL method generalizes unevenly across dynamics and climate contexts.