  Deep Reinforcement Learning has demonstrated the potential of neural networks
tuned with gradient descent for solving complex tasks in well-delimited
environments. However, these neural systems are slow learners producing
specialized agents with no mechanism to continue learning beyond their training
curriculum. On the contrary, biological synaptic plasticity is persistent and
manifold, and has been hypothesized to play a key role in executive functions
such as working memory and cognitive flexibility, potentially supporting more
efficient and generic learning abilities. Inspired by this, we propose to build
networks with dynamic weights, able to continually perform self-reflexive
modification as a function of their current synaptic state and action-reward
feedback, rather than a fixed network configuration. The resulting model,
MetODS (for Meta-Optimized Dynamical Synapses) is a broadly applicable
meta-reinforcement learning system able to learn efficient and powerful control
rules in the agent policy space. A single layer with dynamic synapses can
perform one-shot learning, generalizes navigation principles to unseen
environments and manifests a strong ability to learn adaptive motor policies.
