ArticlePloS one2025
Combining meta reinforcement learning with neural plasticity mechanisms for improved AI performance.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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2 authors.
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Abstract
This research explores the potential of combining Meta Reinforcement Learning (MRL) with Spike-Timing-Dependent Plasticity (STDP) to enhance the performance and adaptability of AI agents in Atari game settings. Our methodology leverages MRL to swiftly adjust agent strategies across a range of games, while STDP fine-tunes synaptic weights based on neuronal spike timings, which in turn improves learning efficiency and decision-making under changing conditions. A series of experiments were conducted with standard Atari games to compare the hybrid MRL-STDP model against baseline models using traditional reinforcement learning techniques like Q-learning and Deep Q-Networks. Various performance metrics, including learning speed, adaptability, and cross-game generalization, were evaluated. The results show that the MRL-STDP approach significantly accelerates the agent's ability to reach competitive performance levels, with a 40% boost in learning efficiency and a 35% increase in adaptability over conventional models.
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