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Reinforcement Learning with Lookahead Information
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Designs model-based RL algorithms (MVP-RL and MVP-TL) that learn optimal policies when agents observe one-step reward or transition lookahead before acting, planning with empirical observation distributions and achieving tight regret bounds.
Problem
Regret minimization in episodic tabular MDPs where agents observe immediate reward or next-state realizations for all actions before choosing an action; prior work only handled known-model planning and naive state augmentation is exponentially large.
Approach
Derives Bellman equations in the original state space for reward and transition lookahead, and plans using the empirical distribution of reward/transition observations (rather than estimated expectations) within an MVP-style optimistic algorithm.
Key results
- MVP-RL achieves regret Õ(sqrt(H^3 S A K)) versus a reward-lookahead baseline, matching the standard episodic RL lower bound up to log factors.
- MVP-TL achieves regret Õ(sqrt(H^2 S K)(sqrt(H)+sqrt(A))) versus a transition-lookahead baseline, the first sublinear regret result for transition lookahead.
- Lookahead can increase value multiplicatively by about AH (reward) and exponentially (transition) relative to no-lookahead agents, implying negative standard regret when sublinear lookahead regret holds.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Provide immediate reward lookahead before choosing an action | RL agents in the two-state prophet-like problem · Two-state prophet-like problem | value gain over no-lookahead: V^{R,*}/V^{no}\approx AH | Helped |
| Provide one-step transition lookahead before choosing an action | RL agents in the chain problem · Chain transition-lookahead example | value: V^{no}=\mathcal{O}(HA^{-H/2}); V^{T,*}=\Omega(H) | Helped |
Surprising findings
- Lookahead information can make the standard regret negative: sublinear lookahead regret implies negative linear standard regret in environments with strict value gains.
Limitations
- Only one-step lookahead is analyzed; multi-step lookahead planning is left open.
- Model-based algorithms are computationally costly (O(KS^2AH) per episode).
- Transition lookahead regret carries a large additive term H^3 S^4 A^3 polylog.
Experimental setup
Metrics: regret
Baselines: MVP (no-lookahead) naive state augmentation
Builds on: MVP (Monotonic Value Propagation) Bellman equations prophet inequality / prophet problems stochastic action sets
Closest papers by meaning
- The Value of Reward Lookahead in Reinforcement Learning (NeurIPS 2024 · similarity 0.92)
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- Partially Observable Reinforcement Learning with Memory Traces (ICML 2025 · similarity 0.88)
- The Distributional Reward Critic Framework for Reinforcement Learning Under Perturbed Rewards (AAAI 2025 · similarity 0.88)
- Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint) (AAAI 2024 · similarity 0.88)
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