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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…

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