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Since most policies depend solely on actions and states/observations, then if you model the space of this field as the Cartesian Product of your state and action spaces, then the policy learns a surface over this combined space, similar to the way a field is parameterized. The policy an agent learns could exhibit the same behavior as the field you describe ...


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Your policy gradient algorithms appear to be working as intended. All standard MDPs have one or more deterministic optimal solutions, and those are the policies that solvers will converge to. Making any of these policies more random will often reduce their effectiveness, making them sub-optimal. So once consistently good actions are discovered, the learning ...


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In the book, the phrase "generate the data" refers to the data from observations about states, actions, next states and rewards, that then get used to make value estimate updates. In both the SARSA and Q learning pseudocode from the book, there is a behaviour policy that selects the next action to take. Other than the initial start state, this ...


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