John Schulman

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2016-217

December 16, 2016

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-217.pdf

This thesis is mostly focused on reinforcement learning, which is viewed as an optimization problem: maximize the expected total reward with respect to the parameters of the policy. The first part of the thesis is concerned with making policy gradient methods more sample-efficient and reliable, especially when used with expressive nonlinear function approximators such as neural networks. Chapter 3 considers how to ensure that policy updates lead to monotonic improvement, and how to optimally update a policy given a batch of sampled trajectories. After providing a theoretical analysis, we propose a practical method called trust region policy optimization (TRPO), which performs well on two challenging tasks: simulated robotic locomotion, and playing Atari games using screen images as input. Chapter 4 looks at improving sample complexity of policy gradient methods in a way that is complementary to TRPO: reducing the variance of policy gradient estimates using a state-value function. Using this method, we obtain state-of-the-art results for learning locomotion controllers for simulated 3D robots.

Reinforcement learning can be viewed as a special case of optimizing an expectation, and similar optimization problems arise in other areas of machine learning; for example, in variational inference, and when using architectures that include mechanisms for memory and attention. Chapter 5 provides a unifying view of these problems, with a general calculus for obtaining gradient estimators of objectives that involve a mixture of sampled random variables and differentiable operations. This unifying view motivates applying algorithms from reinforcement learning to other prediction and probabilistic modeling problems.

Advisors: Pieter Abbeel

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BibTeX citation:

@phdthesis{Schulman:EECS-2016-217,
    Author= {Schulman, John},
    Title= {Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs},
    School= {EECS Department, University of California, Berkeley},
    Year= {2016},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-217.html},
    Number= {UCB/EECS-2016-217},
    Abstract= {
This thesis is mostly focused on reinforcement learning, which is viewed as an optimization problem: maximize the expected total reward with respect to the parameters of the policy. The first part of the thesis is concerned with making policy gradient methods more sample-efficient and reliable, especially when used with expressive nonlinear function approximators such as neural networks. Chapter 3 considers how to ensure that policy updates lead to monotonic improvement, and how to optimally update a policy given a batch of sampled trajectories. After providing a theoretical analysis, we propose a practical method called trust region policy optimization (TRPO), which performs well on two challenging tasks: simulated robotic locomotion, and playing Atari games using screen images as input. Chapter 4 looks at improving sample complexity of policy gradient methods in a way that is complementary to TRPO: reducing the variance of policy gradient estimates using a state-value function. Using this method, we obtain state-of-the-art results for learning locomotion controllers for simulated 3D robots.

Reinforcement learning can be viewed as a special case of optimizing an expectation, and similar optimization problems arise in other areas of machine learning; for example, in variational inference, and when using architectures that include mechanisms for memory and attention. Chapter 5  provides a unifying view of these problems, with a general calculus for obtaining gradient estimators of objectives that involve a mixture of sampled random variables and differentiable operations. This unifying view motivates applying algorithms from reinforcement learning to other prediction and probabilistic modeling problems.},
}

EndNote citation:

%0 Thesis
%A Schulman, John 
%T Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs
%I EECS Department, University of California, Berkeley
%D 2016
%8 December 16
%@ UCB/EECS-2016-217
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-217.html
%F Schulman:EECS-2016-217