Policy gradient methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent. In the off-policy algorithm, actions are sample using behaviour policy and separate target policy is used to optimise for.
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