Richard L. Lewis

Publication Activity (10 Years)

Years Active: 1993-2023
Publications (10 Years): 30

Top Topics

Credit Assignment

Policy Iteration

Reinforcement Learning

Top Venues

Publications

Wilka Carvalho, Angelos Filos, Richard L. Lewis, Honglak Lee, Satinder Singh
Composing Task Knowledge With Modular Successor Feature Approximators. ICLR (2023)
Wilka Carvalho, Andre Saraiva, Angelos Filos, Andrew Kyle Lampinen, Loic Matthey, Richard L. Lewis, Honglak Lee, Satinder Singh, Danilo J. Rezende, Daniel Zoran
Combining Behaviors with the Successor Features Keyboard. CoRR (2023)
Wilka Carvalho, Angelos Filos, Richard L. Lewis, Honglak Lee, Satinder Singh
Composing Task Knowledge with Modular Successor Feature Approximators. CoRR (2023)
Xiaoyang Hu, Shane Storks, Richard L. Lewis, Joyce Chai
In-Context Analogical Reasoning with Pre-Trained Language Models. ACL (1) (2023)
Ethan A. Brooks, Logan Walls, Richard L. Lewis, Satinder Singh
Large Language Models can Implement Policy Iteration. NeurIPS (2023)
Xiaoyang Hu, Shane Storks, Richard L. Lewis, Joyce Chai
In-Context Analogical Reasoning with Pre-Trained Language Models. CoRR (2023)
Wilka Carvalho, Andre Saraiva, Angelos Filos, Andrew K. Lampinen, Loic Matthey, Richard L. Lewis, Honglak Lee, Satinder Singh, Danilo Jimenez Rezende, Daniel Zoran
Combining Behaviors with the Successor Features Keyboard. NeurIPS (2023)
Zeyu Zheng, Risto Vuorio, Richard L. Lewis, Satinder Singh
Adaptive Pairwise Weights for Temporal Credit Assignment. AAAI (2022)
Vivek Veeriah, Zeyu Zheng, Richard L. Lewis, Satinder Singh
GrASP: Gradient-Based Affordance Selection for Planning. CoRR (2022)
Ethan A. Brooks, Logan Walls, Richard L. Lewis, Satinder Singh
In-Context Policy Iteration. CoRR (2022)
Zeyu Zheng, Vivek Veeriah, Risto Vuorio, Richard L. Lewis, Satinder Singh
Learning State Representations from Random Deep Action-conditional Predictions. CoRR (2021)
Wilka Carvalho, Anthony Liang, Kimin Lee, Sungryull Sohn, Honglak Lee, Richard L. Lewis, Satinder Singh
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment. IJCAI (2021)
Soo-Hyun Ryu, Richard L. Lewis
Accounting for Agreement Phenomena in Sentence Comprehension with Transformer Language Models: Effects of Similarity-based Interference on Surprisal and Attention. CMLS (2021)
Soo-Hyun Ryu, Richard L. Lewis
Accounting for Agreement Phenomena in Sentence Comprehension with Transformer Language Models: Effects of Similarity-based Interference on Surprisal and Attention. CoRR (2021)
Ethan A. Brooks, Janarthanan Rajendran, Richard L. Lewis, Satinder Singh
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions. CoRR (2021)
Zeyu Zheng, Vivek Veeriah, Risto Vuorio, Richard L. Lewis, Satinder Singh
Learning State Representations from Random Deep Action-conditional Predictions. NeurIPS (2021)
Zeyu Zheng, Risto Vuorio, Richard L. Lewis, Satinder Singh
Pairwise Weights for Temporal Credit Assignment. CoRR (2021)
Ethan A. Brooks, Janarthanan Rajendran, Richard L. Lewis, Satinder Singh
Reinforcement Learning of Implicit and Explicit Control Flow Instructions. ICML (2021)
Janarthanan Rajendran, Richard L. Lewis, Vivek Veeriah, Honglak Lee, Satinder Singh
How Should an Agent Practice? AAAI (2020)
Wilka Carvalho, Anthony Liang, Kimin Lee, Sungryull Sohn, Honglak Lee, Richard L. Lewis, Satinder Singh
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in First-person Simulated 3D Environments. CoRR (2020)
Janarthanan Rajendran, Richard L. Lewis, Vivek Veeriah, Honglak Lee, Satinder Singh
How Should an Agent Practice? CoRR (2019)
Qi Zhang, Richard L. Lewis, Satinder P. Singh, Edmund H. Durfee
Learning to Communicate and Solve Visual Blocks-World Tasks. AAAI (2019)
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
Discovery of Useful Questions as Auxiliary Tasks. NeurIPS (2019)
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Richard L. Lewis, Janarthanan Rajendran, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
Discovery of Useful Questions as Auxiliary Tasks. CoRR (2019)
Pyeong Whan Cho, Matthew Goldrick, Richard L. Lewis, Paul Smolensky
Dynamic encoding of structural uncertainty in gradient symbols. CMCL (2018)
Aditya Acharya, Xiuli Chen, Christopher W. Myers, Richard L. Lewis, Andrew Howes
Human Visual Search as a Deep Reinforcement Learning Solution to a POMDP. CogSci (2017)
Xiaoxiao Guo, Satinder P. Singh, Richard L. Lewis, Honglak Lee
Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games. CoRR (2016)
Xiaoxiao Guo, Satinder P. Singh, Richard L. Lewis, Honglak Lee
Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games. IJCAI (2016)
Andrew Howes, Geoffrey B. Duggan, Kiran Kalidindi, Yuan-Chi Tseng, Richard L. Lewis
Predicting Short-Term Remembering as Boundedly Optimal Strategy Choice. Cogn. Sci. 40 (5) (2016)
Nan Jiang, Alex Kulesza, Satinder P. Singh, Richard L. Lewis
The Dependence of Effective Planning Horizon on Model Accuracy. IJCAI (2016)
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, Satinder P. Singh
Action-Conditional Video Prediction using Deep Networks in Atari Games. NIPS (2015)
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, Satinder P. Singh
Action-Conditional Video Prediction using Deep Networks in Atari Games. CoRR (2015)
Nan Jiang, Alex Kulesza, Satinder P. Singh, Richard L. Lewis
The Dependence of Effective Planning Horizon on Model Accuracy. AAMAS (2015)
Xiaoxiao Guo, Satinder P. Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning. NIPS (2014)
Michael Shvartsman, Richard L. Lewis, Satinder P. Singh
Computationally Rational Saccadic Control: An Explanation of Spillover Effects Based on Sampling from Noisy Perception and Memory. CMCL@ACL (2014)
Bingyao Liu, Satinder P. Singh, Richard L. Lewis, Shiyin Qin
Optimal Rewards for Cooperative Agents. IEEE Trans. Auton. Ment. Dev. 6 (4) (2014)
Nan Jiang, Satinder P. Singh, Richard L. Lewis
Improving UCT planning via approximate homomorphisms. AAMAS (2014)
Andrew Howes, Richard L. Lewis, Satinder P. Singh
Utility Maximization and Bounds on Human Information Processing. Top. Cogn. Sci. 6 (2) (2014)
Richard L. Lewis, Andrew Howes, Satinder P. Singh
Computational Rationality: Linking Mechanism and Behavior Through Bounded Utility Maximization. Top. Cogn. Sci. 6 (2) (2014)
Christopher W. Myers, Richard L. Lewis, Andrew Howes
Bounded Optimal State Estimation and Control in Visual Search: Explaining Distractor Ratio Effects. CogSci (2013)
Xiaoxiao Guo, Satinder P. Singh, Richard L. Lewis
Reward Mapping for Transfer in Long-Lived Agents. NIPS (2013)
Michael Feary, Dorrit Billman, Xiuli Chen, Andrew Howes, Richard L. Lewis, Lance Sherry, Satinder P. Singh
Linking Context to Evaluation in the Design of Safety Critical Interfaces. HCI (1) (2013)
Richard L. Lewis, Michael Shvartsman, Satinder P. Singh
The Adaptive Nature of Eye Movements in Linguistic Tasks: How Payoff and Architecture Shape Speed-Accuracy Trade-Offs. Top. Cogn. Sci. 5 (3) (2013)
John Tracy Hale, Richard L. Lewis, David Reitter, Chung-chieh Shan, Sashank Varma
Integrating Language and Cognitive Architecture. AAAI Fall Symposia (2013)
Bingyao Liu, Satinder P. Singh, Richard L. Lewis, Shiyin Qin
Optimal rewards in multiagent teams. ICDL-EPIROB (2012)
Jonathan Sorg, Satinder P. Singh, Richard L. Lewis
Variance-Based Rewards for Approximate Bayesian Reinforcement Learning CoRR (2012)
Jeshua Bratman, Satinder P. Singh, Jonathan Sorg, Richard L. Lewis
Strong mitigation: nesting search for good policies within search for good reward. AAMAS (2012)
Jonathan Sorg, Satinder P. Singh, Richard L. Lewis
Optimal Rewards versus Leaf-Evaluation Heuristics in Planning Agents. AAAI (2011)
Satinder P. Singh, Richard L. Lewis, Andrew G. Barto, Jonathan Sorg
Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective. IEEE Trans. Auton. Ment. Dev. 2 (2) (2010)
Jonathan Sorg, Satinder P. Singh, Richard L. Lewis
Internal Rewards Mitigate Agent Boundedness. ICML (2010)
Jonathan Sorg, Satinder P. Singh, Richard L. Lewis
Reward Design via Online Gradient Ascent. NIPS (2010)
Jonathan Sorg, Satinder P. Singh, Richard L. Lewis
Variance-Based Rewards for Approximate Bayesian Reinforcement Learning. UAI (2010)
Robert P. Marinier III, John E. Laird, Richard L. Lewis
A computational unification of cognitive behavior and emotion. Cogn. Syst. Res. 10 (1) (2009)
Shravan Vasishth, Sven Brüssow, Richard L. Lewis, Heiner Drenhaus
Processing Polarity: How the Ungrammatical Intrudes on the Grammatical. Cogn. Sci. 32 (4) (2008)
Katherine Eng, Richard L. Lewis, Irene Tollinger, Alina Chu, Andrew Howes, Alonso H. Vera
Generating automated predictions of behavior strategically adapted to specific performance objectives. CHI (2006)
Irene Tollinger, Richard L. Lewis, Michael McCurdy, Preston Tollinger, Alonso H. Vera, Andrew Howes, Laura Pelton
Supporting efficient development of cognitive models at multiple skill levels: exploring recent advances in constraint-based modeling. CHI (2005)
Richard L. Lewis, Shravan Vasishth
An Activation-Based Model of Sentence Processing as Skilled Memory Retrieval. Cogn. Sci. 29 (3) (2005)
Richard L. Lewis, Alonso H. Vera, Andrew Howes
A Constraint-Based Approach to Understanding the Composition of Skill. ICCM (2004)
Alonso H. Vera, Andrew Howes, Michael McCurdy, Richard L. Lewis
A constraint satisfaction approach to predicting skilled interactive cognition. CHI (2004)
David M. Steier, Richard L. Lewis, Jill Fain Lehman, Anna L. Zacherl
Combining Multiple Knowledge Sources in an Integrated Intelligent System. IEEE Expert 8 (3) (1993)