Neuromorphic Engineering
Policy gradient methods are a class of reinforcement learning techniques that optimize the policy directly by adjusting its parameters based on the gradients of expected rewards. This approach contrasts with value-based methods, as it focuses on learning the best action to take in a given state, thereby allowing for the optimization of stochastic policies. These methods are particularly useful in environments with high-dimensional action spaces or continuous actions, where traditional methods may struggle.
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