Researchers at DeepMind Trained a Semi-Parametric Reinforcement Learning RL Architecture to Retrieve and Use Relevant Information from Large Datasets of Experience

In our day-to-day life, humans make a lot of decisions. Flexibly applying prior experiences to a novel scenario is required for effective decision-making. One might wonder how reinforcement learning (RL) agents use relevant information to make decisions? Deep RL agents are often depicted as a monolithic parametric function that has been taught to amortize meaningful […]

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