non-linear value function approximation: double deep q

Non-linear Value Function Approximation: Double Deep Q-Networks

Alina Vereshchaka

CSE4/510 Reinforcement LearningFall 2019

avereshc@buffalo.edu

October 8, 2019

*Slides are based on Deep Reinforcement Learning: Q-Learning by Garima Lalwani, Karan Ganju, UnnatJain. Illinois

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Overview

1 Recap: DQN

2 Double Deep Q Network

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Table of Contents

1 Recap: DQN

2 Double Deep Q Network

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Recap: Deep Q-Networks (DQN)

Represent value function by deep Q-network with weights w

Q(s, a,w) ≈ Qπ(s, a)

Define objective function

Leading to the following Q-leaning gradient

Optimize objective end-to-end by SGD, using ∂L(w)∂w

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Deep Q-Networks

DQN provides a stable solution to deep value-based RL

1 Use experience replay

2 Freeze target Q-network

3 Clip rewards or normalize network adaptive to sensible range

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Deep Q-Network (DQN) Architecture

Naive DQN Optimized DQN used by DeepMind

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DQN in Atari

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DQN Algorithm

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Table of Contents

1 Recap: DQN

2 Double Deep Q Network

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Double Q learning

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Double Q learning

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Double Q-learning

Two estimators:

Estimator Q1: Obtain best actions

Estimator Q2: Evaluate Q for the above action

What is the main motivation?

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Double Q-learning

Two estimators:

Estimator Q1: Obtain best actions

Estimator Q2: Evaluate Q for the above action

What is the main motivation?

Alina Vereshchaka (UB) CSE4/510 Reinforcement Learning, Lecture 12 October 8, 2019 12 / 18

Double Q-learning

Two estimators:

Estimator Q1: Obtain best actions

Estimator Q2: Evaluate Q for the above action

Chances of both estimators overestimating at sameaction is lesser

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Double Q-learning

Two estimators:

Estimator Q1: Obtain best actions

Estimator Q2: Evaluate Q for the above action

Q1(s, a)← Q1(s, a) + α(Target− Q1(s, a))

Q Target: r(s, a) + γmaxa′ Q1(s′, a′)

Double Q Target: r(s, a) + γQ2(s′, argmaxa′(Q1(s

′, a′)))

Alina Vereshchaka (UB) CSE4/510 Reinforcement Learning, Lecture 12 October 8, 2019 14 / 18

Double Q-learning

Two estimators:

Estimator Q1: Obtain best actions

Estimator Q2: Evaluate Q for the above action

Q1(s, a)← Q1(s, a) + α(Target− Q1(s, a))

Q Target: r(s, a) + γmaxa′ Q1(s′, a′)

Double Q Target: r(s, a) + γQ2(s′, argmaxa′(Q1(s

′, a′)))

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Double Q-learning

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Double Deep Q Network

Two estimators:

Estimator Q1: Obtain best actions

Estimator Q2: Evaluate Q for the above action

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Double Deep Q Network

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Are the Q-values accurate?

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