Value-Preserving Action Quantization
Search-based policy learning, such as Monte Carlo tree search (MCTS), offers the flexibility to trade runtime computation for data efficiency and adaptation to novel environments. For robot learning, however, applying search requires discretizing the continuous action space and associated dynamics, typically through action quantization. While reconstruction-based quantization is sufficient for representing the continuous action space alone, policy learning additionally requires the quantized actions to preserve their effects on future states and values. We show that such requirement is not necessarily captured by action reconstruction error. Instead, we prove that the error in the value f...
| Authors | Max Muchen Sun, Jueun Kwon, Benjamin Riviere et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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