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Exploration in Structured Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use the Lipschitz structure to direct exploration rather than explore state-action pairs without exploiting structure | DEL · Lipschitz toy MDP | regret | Helped |
| use the Lipschitz feasible set instead of the unstructured feasible set | Simplified DEL · Lipschitz toy MDP | regret growth with number of states | Helped |
| direct exploration toward the solution of the structure-specific lower-bound optimization | DEL · General structured ergodic MDPs | asymptotic regret: (1+\gamma) K_{\Phi}(\phi) | Helped |
| exploit Lipschitz continuity of transitions and rewards | DEL · Lipschitz MDPs | regret scaling with state and action space sizes: \frac{(H+1)^3}{\delta^2_{\min}} S_{\textnormal{lip}} A_{\textnormal{lip}}\log T | Helped |
Closest papers by meaning
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- Improved Bounds for Reward-Agnostic and Reward-Free Exploration (ICML 2026 · similarity 0.90)
- Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling (ICML 2024 · similarity 0.90)
- Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs (NeurIPS 2024 · similarity 0.89)
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