Benchmarking Reinforcement Learning via Stochastic Converse Optimality: Generating Systems with Known Optimal Policies
#reinforcement learning #benchmarking #optimal policies #stochastic converse optimality #evaluation
📌 Key Takeaways
- Researchers propose a method to generate benchmark environments with known optimal policies for RL evaluation.
- The approach uses stochastic converse optimality to create systems where optimal solutions are pre-defined.
- This enables more accurate and reliable benchmarking of reinforcement learning algorithms.
- The method addresses the challenge of evaluating RL performance without ground truth optimal policies.
📖 Full Retelling
arXiv:2603.17631v1 Announce Type: cross
Abstract: The objective comparison of Reinforcement Learning (RL) algorithms is notoriously complex as outcomes and benchmarking of performances of different RL approaches are critically sensitive to environmental design, reward structures, and stochasticity inherent in both algorithmic learning and environmental dynamics. To manage this complexity, we introduce a rigorous benchmarking framework by extending converse optimality to discrete-time, control-a
🏷️ Themes
Reinforcement Learning, Benchmarking
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Original Source
arXiv:2603.17631v1 Announce Type: cross
Abstract: The objective comparison of Reinforcement Learning (RL) algorithms is notoriously complex as outcomes and benchmarking of performances of different RL approaches are critically sensitive to environmental design, reward structures, and stochasticity inherent in both algorithmic learning and environmental dynamics. To manage this complexity, we introduce a rigorous benchmarking framework by extending converse optimality to discrete-time, control-a
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