Near-Optimal Reinforcement Learning with Multi-Step Transition Lookahead
We study reinforcement learning (RL) with transition look-ahead, where the agent may observe which states would be visited upon playing any sequence of actions before deciding its course of action. Although look-ahead can substantially improve achievable performance, [1] showed that optimal planning with multi-step transition look-ahead is NP-hard. However, this hardness was established using a discount factor close to one. It was therefore unknown whether the problem remains hard for every discount factor, and whether near-optimal planning can nevertheless be performed efficiently. We resolve both questions. First, we show that for every fixed discount factor, exact planning remains NP-h...
| Authors | Corentin Pla, Hugo Richard, Marc Abeille et al. |
|---|---|
| Primary category | stat.ML |
| Published | 10 September 2026 |
| Last updated at source | 28 September 2026 |
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