Study on generalization in reparameterizable RL, deriving new guarantees.
problem Understanding generalization in reparameterizable RL.
method Using supervised learning and transfer learning theory, derived guarantees on the gap between expected and empirical return.
result Generalization capability of reparameterizable RL is related to multiple factors including smoothness of the environment transition, reward, and policy function class.