The ability to learn from experience is a deeply fundamental process, one that is subject to significant research in neuroscience; after all, how does experience shape our neurological structure…

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Reinforcement learning (RL) is inspired by the process of learning from experience, with the Soft Actor-Critic (SAC) algorithm being a popular framework. This post discusses the mathematical foundation of SAC agents, detailing the actor (policy) and critic networks. The actor network uses a neural network to estimate actions and their probabilities while the critic network estimates the expected return of action-state pairs. Python code snippets in PyTorch demonstrate the implementation of these networks and their integration into a RL model.

Mathematical Foundation Underpinning Reinforcement Learning