Machine Learning, Optimization, Gradient Descent, Reinforcement Learning.
is just a variant of REINFORCE: Here the baseline can be interpreted as a learned value function c_ϕ(s_t). Now let’s consider LAX in the RL setting: Here the baseline c_ϕ depends on the action and can be interpreted as a learned Q-function. The gradient is unbiased because we add the