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4f20f7f5d2e7a1b640ebc8244428558c-Paper.pdf

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Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V. Le, Ni Lao

Neural Information Processing Systems

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Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

Neural Information Processing Systems

Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence.

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Convergent Policy Optimization for Safe Reinforcement Learning

Ming Yu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang

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Given ,J ( )andD ( )arethesample (i.e., atrajectory) . Note J ( ) and D ( ) are randomness J ( )andD ( )todenote anda ClearlyweJ( )= E J ( ) andD( )= E D ( ) .

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  Technology: Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.51)

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M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search

Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao

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End-to-End Differentiable Physics for Learning and Control

Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum, J. Zico Kolter

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Neural Information Processing Systems http://nips.cc/

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  Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.48)

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Importance Resamplingfor Off-policy Prediction

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Thoughunbiased, IScanbehigh-variance. Alowervariancealternativeis Weighted IS (WIS). Figure 4: Learning Ratesensitivityplotsinthe Random Walk Markov Chain, withbuffersizen = 15000 andmini-batchsizek = 16.

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Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards

Siyuan Li, Rui Wang, Minxue Tang, Chongjie Zhang

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N experiences h and l forT low-le low-lerlt defined thehigh-lei-thiteration. themodifiedi-thiteration.

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Multiagent Evaluation under Incomplete Information

Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Perolat, Michal Valko, Georgios Piliouras, Remi Munos

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Neural Information Processing Systems http://nips.cc/

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DeepReinforcementLearningattheEdgeofthe StatisticalPrecipice

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Research in artificial intelligence, and particularly deep reinforcement learning (RL), relies on evaluating aggregate performance on a diverse suite of tasks to assess progress.

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