Policy Gradient Methods: Variance Reduction and Stochastic Convergence
In a reinforcement learning task an agent must learn a policy for performing actions so as to perform well in a given environment. Policy gradient methods consider a parameterized class of policies, and using a policy from the class, and a trajectory through the environment taken by the agent using this policy, estimate the performance of the policy with respect to the parameters. Policy gradient methods avoid some of the problems of value function methods, such as policy degradation,...[Show more]
|Collections||Open Access Theses|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.