Model-Based Reinforcement Learning
A reinforcement learning paradigm that improves sample efficiency by explicitly learning a model of environment dynamics (a world model), which the agent uses for planning or generating virtual experience.
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A study titled "Spacetime Emergence in the Memory of a Learning Agent" shows that by training a model-based reinforcement learning agent to complete a quantum information task, the null structure of spacetime can spontaneously emerge from the agent's internal predictive model.
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Model-based reinforcement learning improves sample efficiency by learning a dynamics model of the environment for planning, with representative methods including Dreamer and MBPO.
75%UnverifiedA study titled "Spacetime Emergence in the Memory of a Learning Agent" shows that by training a model-based reinforcement learning agent to complete a quantum information task, the null structure of spacetime can spontaneously emerge from the agent's internal predictive model.
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