DeepMind Boosts RL Agents' Retrieval Capability to Tens of Millions of Pieces of Information

The conventional approach for improving the decision-making of deep reinforcement learning (RL) agents is to gradually amortize the useful information they gain from their experiences via gradient descent on training losses. This method however requires building increasingly large models to deal with increasingly complex environments and is difficult to adapt to novel situations. Although adding information sources can benefit agent performance, there is currently no end-to-end solution for enabling agents to attend to information outside their working memory to inform their actions. In the new paper Large-Scale Retrieval for Reinforcement Learning, a DeepMind research team introduces a novel approach that dramatically expands the information accessible to reinforcement learning (RL) agents, enabling them to attend to tens of millions of information pieces, incorporate new information without retraining, and learn in an end-to-end manner how to use this information in their decision making. In the work, the team trains a semiparametric model-based agent to predict future policies and values conditioned on future actions in a given state and adds a retrieval mechanism to enable the model to draw from information in a large-scale dataset to inform its predictions.