From the optimal action conditionals, we recover the optimal policy through backward messages, relate it to value functions in RL, and connect probabilistic inference to maximum entropy reinforcement learning.

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Building on part 1, we establish decision-making as a probabilistic graphical model, connect RL to a trajectory prediction problem, and formulate optimal trajectory prediction as probabilistic inference.

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This series of posts explores the intersection of reinforcement learning and probabilistic graphical models, delving into the optimization of policies through inference, bridging the gap between planning and decision-making under uncertainty.

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