Evolutionary Discovery of Developmental Reward Schedules in Deep Reinforcement Learning

2026-07-13 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

An evolutionary framework is proposed for discovering developmental reward schedules in deep reinforcement learning (RL), combining three biologically inspired motivational components (agency, novelty, reactivity) with time-varying weights. Evaluated on two sparse-reward MiniGrid tasks, DoorKey-6x6 and KeyCorridorS3R1, the framework compared four evolutionary algorithms (CMA-ES, xNES, DE, L-SHADE) against an extrinsically motivated baseline and three hand-designed methods. On DoorKey-6x6, all evolved methods outperformed baselines, with L-SHADE achieving an approximate relative mean improvement of 11.4% over the extrinsic-only baseline. On KeyCorridorS3R1, CMA-ES performed best, while other evolved methods showed weaker generalization. The discovered schedules consistently prioritized novelty as the dominant early signal, diverging from the defined biological developmental ordering. The project's code is available at https://github.com/alannadels/Evolutionary_RL.git.

Key takeaway

For Machine Learning Engineers designing reward functions for sparse-reward deep RL tasks, you should consider evolutionary optimization to discover dynamic reward schedules. Hand-designed or biologically inspired fixed schedules can hinder learning, as demonstrated by the framework's findings where evolved schedules outperformed baselines. Focus on early novelty signals, as computational evolution consistently prioritized this over agency or reactivity in initial training phases.

Key insights

Evolutionary optimization can discover effective, task-specific developmental reward schedules in deep RL that diverge from biological sequences.

Principles

• Reward schedule dynamics are crucial for RL task performance.
• Computational evolution may find different optimal motivational sequences than biological evolution.
• Per-task adaptation of reward schedules is essential for effective learning.

Method

An evolutionary outer loop optimizes piecewise linear weights for agency, novelty, and reactivity components in a composite reward function. An inner loop trains PPO agents, with fitness based on sparse task reward performance.

In practice

• Use evolutionary algorithms (e.g., L-SHADE, CMA-ES) to optimize RL reward schedules.
• Prioritize novelty as an early training signal in sparse-reward environments.
• Test different evolutionary algorithm families based on task difficulty.

Topics

• Deep Reinforcement Learning
• Evolutionary Algorithms
• Reward Shaping
• Intrinsic Motivation
• MiniGrid Environments
• Developmental Learning

Code references

• alannadels/Evolutionary_RL

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.