Comparison of MRL-STDP with DQN and PPO.

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.