Meta-Learning through Hebbian Plasticity in Random Networks (Paper Explained)

#ai #neuroscience #rl Reinforcement Learning is a powerful tool, but it lacks biological plausibility because it learns a fixed policy network. Animals use neuroplasticity to reconfigure their policies on the fly and quickly adapt to new situations. This paper uses Hebbian Learning, a biologically inspired technique, to have agents adapt random networks to high-performing solutions as an episode is progressing, leading to agents that can reconfigure themselves in response to new observations. OUTLINE: 0:00 - Intro & Overview 2:30 - Reinforcement Learning vs Hebbian Plasticity 9:00 - Episodes in Hebbian Learning 10:00 - Hebbian Plasticity Rules 18:10 - Quadruped Experiment Results 21:20 - Evolutionary Learning of Hebbian Plasticity 29:10 - More Experimental Results 34:50 - Conclusions 35:30 - Broader Impact Statement Videos: https://twitter.com/risi1979/status/1280544779630186499 Paper: https://arxiv.org/abs/2007.02686 Abstract: Lifelong learning and adaptability are two defining aspects of biological agents. Modern reinforcement learning (RL) approaches have shown significant progress in solving complex tasks, however once training is concluded, the found solutions are typically static and incapable of adapting to new information or perturbations. While it is still not completely understood how biological brains learn and adapt so efficiently from experience, it is believed that synaptic plasticity plays a prominent role in this process. Inspired by this biological mechanism, we propose a search method that, instead of optimizing the weight parameters of neural networks directly, only searches for synapse-specific Hebbian learning rules that allow the network to continuously self-organize its weights during the lifetime of the agent. We demonstrate our approach on several reinforcement learning tasks with different sensory modalities and more than 450K trainable plasticity parameters. We find that starting from completely random weights, the discovered Hebbian r