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Evolutionary algorithms discover novel reward schedules for reinforcement learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an evolutionary framework to discover developmental reward schedules in deep reinforcement learning, aiming to explore how motivational priorities can shift during training. This approach combines three biologically inspired components—agency, novelty, and reactivity—with dynamically changing weights. When tested on sparse-reward MiniGrid tasks, the evolutionary methods, particularly L-SHADE and CMA-ES, showed improved performance and generalizability over hand-designed baselines. Interestingly, the discovered schedules often prioritized novelty as an early training signal, diverging from typical biological developmental patterns. AI

IMPACT This research could lead to more efficient and adaptable reinforcement learning agents by automating the design of reward structures.

RANK_REASON This is a research paper detailing a novel method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Evolutionary algorithms discover novel reward schedules for reinforcement learning

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This is a research paper detailing a novel method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Alan Nadelsticher Ruvalcaba ·

    Evolutionary Discovery of Developmental Reward Schedules in Deep Reinforcement Learning

    The temporal structure of reward composition in reinforcement learning (RL) is typically hand-designed and held fixed throughout training, leaving the progression of motivational priorities largely unexplored. In this work, we propose an evolutionary framework for discovering dev…

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