Reinforcement Learning (RL) is a type of machine learning where agents learn to make decisions by receiving rewards or penalties for their actions. In neuroscience, RL has been used to model how the brain might implement learning and decision-making processes. Early work in this field focused on the role of dopamine as a reward prediction error signal, a concept that has been influential in understanding how the brain processes rewards. More recent work has explored the idea of distributional reinforcement learning, which suggests that the brain might represent a distribution of possible future rewards rather than a single expected value. This approach has led to new insights into how the brain might handle uncertainty and variability in the environment. Theoretical advances in RL have been closely linked to experimental findings in neuroscience, leading to increasingly complex models that aim to capture the nuances of brain function. Key areas of interest include model-free and model-based RL algorithms, deep reinforcement learning, and meta-reinforcement learning, each offering different perspectives on how the brain might learn and adapt to its environment.
Temporal difference algorithms, model-free and model-based RL
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Modeling brain learning processes, handling uncertainty
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0.00
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01/01/1970
01/01/1970
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Yes