Research map / Reinforcement learning / Visual and representation RL
When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Gives a theoretical characterization of when deterministic skills (macroactions, options) help RL in deterministic sparse-reward finite-action MDPs, via learning and exploration difficulty metrics and an incompressibility measure.
Problem
When and why do temporal abstractions (skills) improve RL sample efficiency? Existing difficulty metrics for MDPs do not capture sample complexity of RL in deterministic sparse-reward environments, and no precise characterization of when skills help existed.
Approach
Defines p-learning difficulty and p-exploration difficulty for deterministic sparse-reward MDPs, and an incompressibility measure of solutions; proves lower bounds on how skills change these difficulties, showing skills help exploration more than learning and that macroactions can provably hurt.
Key results
- Combined p-learning and p-exploration difficulty correlates with log sample complexity; most correlations at least around 0.7 (e.g., 0.947 for Q-learning on CliffWalking).
- Value iteration convergence iterations correlate above 0.9 with p-weighted average solution length in most cases.
- HRL with LOVE options improves sample complexity on 8Puzzle (ratio 0.64) and RubiksCube222 (ratio 0.73) but gives large gains on CliffWalking (ratio 0.000007) and CompILE2 (0.00023), with improvement decreasing as unmerged incompressibility increases.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| apply HRL with options discovered by LOVE | the four studied environments · CliffWalking, CompILE2, 8Puzzle, and RubiksCube222 | sample complexity: 0.000007 ± 0.000007; 0.00023 ± 0.00011; 0.64 ± 0.19; 0.73 ± 0.17 | Mixed |
| use the geometric mean instead of the arithmetic mean in p-exploration difficulty (ablation) | the combined difficulty metric predicting RL sample complexity · CliffWalking, CompILE2, 8Puzzle, and RubiksCube222 | correlation with log sample complexity | Helped |
| use macroactions in environments with lower unmerged p-incompressibility | RL skill augmentation · four studied environments | best complexity improvement ratio | Helped |
Surprising findings
- Macroactions can provably increase p-learning difficulty and p-exploration difficulty in some environments regardless of how many macroactions are added.
- Skills appear more useful for improving exploration than for learning from existing experience.
Limitations
- Results restricted to deterministic sparse-reward environments with finite action spaces.
- Experiments limited to small environments (state space up to ~3.7M) where difficulty metrics are computable.
- Difficulty metrics are rough heuristic estimates of sample complexity.
Experimental setup
Datasets: CliffWalking CompILE2 8Puzzle RubiksCube222
Benchmarks: CliffWalking CompILE2 8Puzzle RubiksCube222
Metrics: Pearson correlation of log sample complexity with log diffic sample complexity ratio N+/N0 correlation of incompressibility with best complexity improv
Baselines: LEMMA LOVE Q-learning value iteration REINFORCE DQN
Builds on: LEMMA LOVE Option-Critic DQN Q-learning
Closest papers by meaning
- Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills (ICML 2020 · similarity 0.90)
- A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals (ICLR 2025 · similarity 0.89)
- DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning (NeurIPS 2025 · similarity 0.89)
- Efficient Skill Discovery via Regret-Aware Optimization (ICML 2025 · similarity 0.88)
- Reconciling Spatial and Temporal Abstractions for Goal Representation (ICLR 2024 · similarity 0.87)
- Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization (NeurIPS 2026 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.