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Constructing an Optimal Behavior Basis for the Option Keyboard
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Introduces Option Keyboard Basis (OKB), a method that incrementally constructs a small behavior basis so that the option keyboard can zero-shot optimally solve any linear-reward task, and is strictly more expressive than a convex coverage set.
Problem
Identifying an optimal, small set of base policies (behavior basis) for the option keyboard so that optimal policies for any linear-reward task can be identified zero-shot, avoiding the exponential cost of computing a full convex coverage set.
Approach
Iteratively adds optimal base policies for corner weights where the option keyboard's meta-policy cannot express the optimal action (tested via a positive-advantage criterion), trains a meta-policy over selected weights with actor-critic, and removes dominated policies, yielding a basis no larger than the CCS and provably sufficient for optimality.
Key results
- OKB reaches near-optimal performance with just 2-3 base policies in Minecart and Highway, consistently outperforming SFOLS and OKB-Uniform.
- On FetchPickAndPlace, the performance gap between OKB and SFOLS widens as the number of reward features grows from d=2 to d=8.
- On the non-linear Item Collection task, OKB solves the task more quickly than SIP, while DQN fails.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use OKB instead of SFOLS | linear-task transfer · Minecart | mean normalized return | Helped |
| use OKB instead of SFOLS | linear-task transfer · Highway | mean normalized return | Helped |
| select tasks from the candidate set rather than uniformly from the task space (ablation) | OKB · Highway | mean normalized return | Helped |
| increase the number of reward features | OKB versus SFOLS · FetchPickAndPlace | mean normalized return | Helped |
| increase the number of reward features | OKB versus OKB-Uniform · FetchPickAndPlace | mean normalized return | Mixed |
| use OKB instead of SIP | non-linear task transfer · Item Collection | environment interactions to solve the task | Helped |
| learn the task from scratch with DQN instead of reusing a behavior basis | non-linear task transfer · Item Collection | total reward | Hurt |
Surprising findings
- Proposition showing the option keyboard cannot optimally solve every task in the state-dependent-linear family, even with a full CCS.
Limitations
- Theoretical guarantees assume NewPolicy returns optimal policies and that meta-policy training converges (Assumptions 1 and 2).
- Optimality test is a sufficient but not necessary condition, implemented via a heuristic positive-advantage criterion.
Experimental setup
Datasets: Minecart FetchPickAndPlace Item Collection Highway
Benchmarks: Minecart Highway FetchPickAndPlace Item Collection
Metrics: normalized return mean total reward
Baselines: SFOLS OKB-Uniform SIP DQN GPI
Builds on: Option Keyboard Generalized Policy Improvement Successor Features SFOLS Universal Successor Features Approximators
Closest papers by meaning
- Efficient Multi-Policy Evaluation for Reinforcement Learning (AAAI 2025 · similarity 0.88)
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- On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning (NeurIPS 2025 · similarity 0.87)
- Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization (ICML 2024 · similarity 0.87)
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