Command-Space Counterfactual Explanations for Pareto-Conditioned Reinforcement Learning
A new research paper on arXiv (2608.14963) proposes command-space counterfactual explanations for Pareto Conditioned Networks (PCNs), which are used in multi-objective reinforcement learning. The authors address the opacity of the mapping from command and state to action in PCNs by searching for minimally changed desired-return commands that would cause the policy to choose a foil action. Their contributions include formulating PCN explanations as return-command interventions using a return-only variant, adapting adversarial machine learning methods, and introducing a boundary-seeded directional search. The proposed approach, CF-Z, improves over local optimization in the command-action landscape. The paper is a cross-type announcement, indicating it may have been presented at a conference or journal.
Key facts
- Paper arXiv:2608.14963v1 is a cross-type announcement.
- Proposes command-space counterfactual explanations for Pareto Conditioned Networks (PCNs).
- PCNs condition a single policy on a desired return command for multi-objective reinforcement learning.
- The method searches for minimally changed desired-return commands under which the policy would choose a foil action.
- Uses a return-only PCN variant to avoid horizon-conditioning ambiguity.
- Adapts adversarial machine learning methods to reinforcement-learning explanations.
- Introduces a boundary-seeded directional search for improved optimization.
- The proposed approach is named CF-Z.
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