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Marginal Benefit Driven RL Teacher for Unsupervised Environment Design
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Proposes MBeED and MBeDED, RL-based teacher methods for unsupervised environment design that score generated environments by marginal benefit (Alice vs. outdated Bob policy improvement) and, for MBeDED, select diverse levels via a representative state diversity metric.
Problem
Regret-based environment generation in unsupervised environment design can produce environments that are too difficult to learn from, since regret is an upper bound on learning potential rather than actual learning gain; RL-based teachers like PAIRED are also expensive.
Approach
Replace regret with marginal benefit, the performance difference between the current student policy and a lagged copy of itself on the same generated environment, used as the RL teacher's reward; add a representative-state diversity score (greedy submodular selection over TD-error-important and representative states) to choose which levels stay in the replay buffer.
Key results
- MBeED and MBeDED consistently outperform baselines across most BipedalWalker test environments with faster convergence.
- In Minigrid, MBeDED outperforms REPAIRED and DR on both IQM and optimality gap across 5 runs.
- On OOD F1 CarRacing tracks, MBeED and MBeDED achieve better mean performance than other baselines.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use marginal-benefit-driven environment generation instead of the compared UED methods | MBeED and MBeDED · BipedalWalker test environments | transfer performance | Helped |
| Add representative-state-diversity-guided environment selection to MBeED | MBeDED · BipedalWalker test environments | transfer performance | Helped |
| Use marginal-benefit-driven environment generation and representative-state diversity | MBeDED · Minigrid human-designed test levels | mean return | Mixed |
| Use marginal-benefit-driven environment generation and representative-state diversity | MBeDED · Minigrid OOD test environments | IQM | Helped |
| Use marginal-benefit-driven environment generation and representative-state diversity | MBeDED · Minigrid OOD test environments | optimality gap | Helped |
| Use marginal-benefit-driven environment generation and representative-state diversity | MBeED and MBeDED · CarRacing OOD F1 tracks | zero-shot transfer return | Helped |
Surprising findings
- Regret-based correlation with actual learning gain is positive but with high variance, so high-regret environments do not reliably yield large performance gains.
- RL-based generation (MBeED) yields the lowest level-buffer diversity, which motivated the diversity-guided selection.
Limitations
- Random-based methods such as PLR and ACCEL were excluded from comparison, so the comparison covers only RL-controlled generators.
- Relies on a lagged copy of the student policy, and the diversity metric adds computation for state sampling and greedy selection.
Experimental setup
Datasets: BipedalWalker-Hardcore (modified) Minigrid maze environments CarRacing Bezier-curve tracks
Benchmarks: BipedalWalker test environments Minigrid human-designed mazes CarRacing Formula One tracks (Vanilla, Italy, Germany)
Metrics: mean return IQM optimality gap
Baselines: Domain Randomization Minimax PAIRED REPAIRED PLR ACCEL
Builds on: PAIRED Prioritized Level Replay ACCEL PPO Generalized Advantage Estimation
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- Improving Environment Novelty Quantification for Effective Unsupervised Environment Design (NeurIPS 2024 · similarity 0.88)
- Adversarial Environment Design via Regret-Guided Diffusion Models (NeurIPS 2024 · similarity 0.88)
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