RL Parametrizations: Reinforcement learning (RL) involves designing parametrized policies and value functions to represent the agent’s behavior and estimate the expected rewards. Tate parametrization focuses on modeling the state transition, while action parametrization directly parametrizes the action selection process. Common RL algorithms like DDPG and SAC utilize these parametrizations to optimize policies for complex environments.
