Deep Reinforcement Learning Through Policy Optimization
Reinforcement Learning (Deep RL) has seen several breakthroughs in recent years. In this tutorial we will focus on recent advances in Deep RL through policy gradient methods and actor critic methods. These methods have shown significant success in a wide range of domains, including continuous-action domains such as manipulation, locomotion, and flight. They have also achieved the state of the art in discrete action domains such as Atari. Fundamentally, there are two types of gradient calculations: likelihood ratio gradients (aka score function gradients) and path derivative gradients (aka perturbation analysis gradients). We will teach policy gradient methods of each type, connect with Actor-Critic methods (which learn both a value function and a policy), and cover a generalized view of the computation of gradients of expectations through Stochastic Computation Graphs.
The objective is to provide attendees with a good understanding of foundations as well as recent advances in policy gradient methods and actor critic methods. Approaches that will be taught: Likelihood Ratio Policy Gradient (REINFORCE), Natural Policy Gradient, Trust Region Policy Optimization (TRPO), Generalized Advantage Estimation (GAE), Asynchronous Advantage Actor Critic (A3C), Path Derivative Policy Gradients, (Deep) Deterministic Policy Gradient (DDPG), Stochastic Value Gradients (SVG), Guided Policy Search (GPS). As well as a generalized view of the computation of gradients of expectations through Stochastic Computation Graphs.
Target Audience: Machine learning researchers. RL background not assumed, but some prior familiarity with the basic concepts could be helpful. Good resource: Sutton and Barto Chapters 3 & 4 (http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html).
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