Evolutionary Bilevel Reward Shaping for Generalization in Reinforcement Learning

2026-06-15 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

Generalization via Evolutionary Reward Shaping (GERS) is a novel bilevel optimization approach designed to enhance reinforcement learning (RL) agent generalization to unseen test environments. This method addresses limitations of existing techniques like Domain Randomization (DR), which require diverse training environments and full trajectory observability, often unavailable in privacy-preserving or restricted scenarios. GERS operates with a lower-level RL agent learning a policy on limited training environments with accessible trajectory data, guided by a reward function shaped by an upper level. The upper level employs CMA-ES to optimize reward shaping parameters, maximizing cumulative unshaped reward using only scalar feedback from separate validation environments, without trajectory access. On continuous control tasks, GERS significantly outperforms standard RL baselines on unseen test environments. Its performance is comparable to DR, despite DR utilizing a combined training and validation set with full trajectory access, which GERS does not require for validation.

Key takeaway

For Machine Learning Engineers deploying reinforcement learning agents in environments with restricted data access or privacy concerns, GERS provides a compelling solution for improving generalization. If your project lacks diverse training environments or full trajectory observability, GERS offers a robust alternative to Domain Randomization. You should consider integrating this bilevel optimization approach, which uses scalar validation feedback, to enhance policy performance on unseen test environments without compromising data constraints.

Key insights

GERS improves RL generalization using bilevel optimization and reward shaping with limited data access.

Principles

• Generalization can be enhanced with scalar feedback only.
• Bilevel optimization effectively separates policy learning from reward shaping.
• Reward shaping parameters can be optimized evolutionarily.

Method

GERS uses a lower-level RL agent for policy learning with shaped rewards, while an upper-level CMA-ES optimizes shaping parameters based on scalar validation environment feedback.

In practice

• Apply GERS in privacy-sensitive RL deployments.
• Use CMA-ES for reward shaping parameter optimization.
• Consider GERS when full trajectory data is unavailable.

Topics

• Reinforcement Learning
• Generalization
• Reward Shaping
• Bilevel Optimization
• CMA-ES
• Continuous Control

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.