Reinforcement Learning

2026

Geigh Zollicoffer, Tanush Chopra, Mingkuan Yan, Xiaoxu Ma, Kenneth Eaton, and Mark Riedl
World Model Robustness via Surprise Recognition
Proceedings of the 2026 CVPR Findings (2026).
arXiv Conference bibtexAgentsReinforcement LearningAI Safety

@InProceedings{Zollicoffer2026WorldModel, author = {Zollicoffer, Geigh and Tanush Chopra and Mingkuan Yan and Xiaoxu Ma and Kenneth Eaton and Mark Riedl}, booktitle = {Proceedings of the 2026 CVPR Findings}, title = {World Model Robustness via Surprise Recognition}, year = {2026}, owner = {riedl}, url = {https://arxiv.org/abs/2512.01119}, keywords = {agents, rl, safe}, }

2025

Maduri Singh, Amal Alabdulkarim, Gennie Mansi, and Mark O. Riedl
Explainable Reinforcement Learning Agents Using World Models
Proceedings of the 2025 IJCAI Workshop on Explainable AI (2025).
arXiv Workshop bibtexAgentsExplainable AIReinforcement Learning

@InProceedings{Singh2025ReverseWorldModels, author = {Singh, Maduri and Alabdulkarim, Amal and Mansi, Gennie and Riedl, Mark O.}, booktitle = {Proceedings of the 2025 IJCAI Workshop on Explainable AI}, title = {Explainable Reinforcement Learning Agents Using World Models}, year = {2025}, owner = {riedl}, url = {https://arxiv.org/abs/2505.08073}, keywords = {agents, xai, rl}, }

Geigh Zollicoffer, Kenneth Eaton, Jonathan Balloch, Julia Kim, Riedl Mark O., and Robert Wright
Novelty Detection in Reinforcement Learning with World Models
International Conference on Machine Learning (2025).
arXiv Conference bibtexAgentsReinforcement LearningAI Safety

@InProceedings{Zollicoffer2023Novelty, author = {Geigh Zollicoffer and Kenneth Eaton and Jonathan Balloch and Julia Kim and Riedl Mark O. and Robert Wright}, booktitle = {International Conference on Machine Learning}, title = {Novelty Detection in Reinforcement Learning with World Models}, year = {2025}, owner = {riedl}, url = {https://arxiv.org/abs/2310.08731}, keywords = {agents, rl, safe}, }

Amal Alabdulkarim, Madhuri Singh, Gennie Mansi, Kaely Hall, and Mark O. Riedl
Experiential Explanations for Reinforcement Learning
Neural Computing and Applications 37 (2025).
arXiv Journal bibtexAgentsExplainable AIReinforcement Learning

@Article{Alabdulkarim2025Experiential, author = {Amal Alabdulkarim and Madhuri Singh and Gennie Mansi and Kaely Hall and Riedl, Mark O.}, journal = {Neural Computing and Applications}, title = {Experiential Explanations for Reinforcement Learning}, year = {2025}, volume = {37}, url = {https://arxiv.org/abs/2210.04723}, keywords = {agents, xai, rl}, }

2024

Jonathan C. Balloch, Rishav Bhagat, Geigh Zollicoffer, Ruoran Jia, Julia Kim, and Mark O. Riedl
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning
Proceedings of the 2024 Reinforcement Learning Conference Workshop on Finding the Frame: Examining Conceptual Frameworks in Reinforcement Learning (2024).
arXiv Workshop bibtexAgentsReinforcement Learning

@InProceedings{Balloch2024IsExplorationAllYouNeed, author = {Balloch, Jonathan C. and Rishav Bhagat and Geigh Zollicoffer and Ruoran Jia and Julia Kim and Riedl, Mark O.}, booktitle = {Proceedings of the 2024 Reinforcement Learning Conference Workshop on Finding the Frame: Examining Conceptual Frameworks in Reinforcement Learning}, title = {Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning}, year = {2024}, owner = {riedl}, url = {https://arxiv.org/abs/2404.02235}, keywords = {agents, rl}, }

Christopher Z. Cui, Xiangyu Peng, and Mark O. Riedl
A Mixture-of-Experts Approach to Few-Shot Task Transfer in Open-Ended Text Worlds
arXiv:2405.06059 (2024).
arXiv bibtexInteractive StoriesAgentsReinforcement Learning

@Article{Cui2024MixtureOfExperts, author = {Cui, Christopher Z. and Xiangyu Peng and Mark O. Riedl}, journal = {arXiv:2405.06059}, title = {A Mixture-of-Experts Approach to Few-Shot Task Transfer in Open-Ended Text Worlds}, year = {2024}, owner = {riedl}, url = {https://arxiv.org/abs/2405.06059}, keywords = {int, agents, rl}, }

Rishav Bhagat, Jonathan Balloch, Zhiyu Lin, Julia Kim, and Mark Riedl
External Model Motivated Agents: Reinforcement Learning for Enhanced Environment Sampling
Proceedings of the 2024 Reinforcement Learning Conference Workshop on Reinforcement Learning Beyond Rewards (2024).
arXiv Workshop bibtexAgentsReinforcement Learning

@InProceedings{Bhagat2024ExternalModelMotivatedAgents, author = {Rishav Bhagat and Jonathan Balloch and Zhiyu Lin and Julia Kim and Mark Riedl}, booktitle = {Proceedings of the 2024 Reinforcement Learning Conference Workshop on Reinforcement Learning Beyond Rewards}, title = {External Model Motivated Agents: Reinforcement Learning for Enhanced Environment Sampling}, year = {2024}, owner = {riedl}, url = {https://arxiv.org/abs/2407.00264}, keywords = {agents, rl}, }

Kenneth Eaton, Jonathan Balloch, Julia Kim, and Mark Riedl
The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited
Proceedings of the 2024 Reinforcement Learning Conference Workshop I Can't Believe It's not Better (2024).
arXiv Workshop bibtexAgentsExplainable AIInterpretabilityReinforcement Learning

@InProceedings{Eaton2024InterpretabilityOfCodebooks, author = {Kenneth Eaton and Jonathan Balloch and Julia Kim and Mark Riedl}, booktitle = {Proceedings of the 2024 Reinforcement Learning Conference Workshop I Can't Believe It's not Better}, title = {The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited}, year = {2024}, owner = {riedl}, url = {https://arxiv.org/abs/2407.19532}, keywords = {agents, xai, interp, rl}, }

Zhiyu Lin, Upol Ehsan, Rohan Agarwal, Samihan Dani, Vidushi Vashishth, and Mark Riedl
Beyond Following: Mixing Active Initiative into Computational Creativity
Proceedings of the AAAI Workshop on Experimental AI in Games (2024).
arXiv Workshop bibtexStorytellingReinforcement LearningCreativity

@InProceedings{Lin2024BeyondFollowing, author = {Zhiyu Lin and Upol Ehsan and Rohan Agarwal and Samihan Dani and Vidushi Vashishth and Mark Riedl}, booktitle = {Proceedings of the AAAI Workshop on Experimental {AI} in Games}, title = {Beyond Following: Mixing Active Initiative into Computational Creativity}, year = {2024}, owner = {riedl}, url = {https://arxiv.org/abs/2409.16291}, keywords = {story, rl, create}, }

Ashutosh Baheti, Ximing Lu, Faeze Brahman, Ronan Le Bras, Maarten Sap, and Mark O. Riedl
Improving Language Models with Advantage-based Offline Policy Gradients
Proceedings of ICLR 2024 (2024).
arXiv Conference bibtexLarge Language ModelsReinforcement LearningValue AlignmentAI Safety

@InProceedings{Baheti2023LOL, author = {Ashutosh Baheti and Ximing Lu and Faeze Brahman and Ronan Le Bras and Maarten Sap and Riedl, Mark O.}, booktitle = {Proceedings of ICLR 2024}, title = {Improving Language Models with Advantage-based Offline Policy Gradients}, year = {2024}, owner = {riedl}, url = {https://arxiv.org/abs/2305.14718}, keywords = {llms, rl, align, safe}, }

2023

Xiangyu Peng, Christopher Cui, Wei Zhou, Renee Jia, and Mark O. Riedl
Story Shaping: Teaching Agents Human-like Behavior with Stories
Proceedings of the 2023 AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (2023).
arXiv Conference bibtexStorytellingInteractive StoriesAgentsReinforcement LearningValue Alignment

@InProceedings{Peng2023Shaping, author = {Xiangyu Peng and Christopher Cui and Wei Zhou and Renee Jia and Riedl, Mark O.}, booktitle = {{Proceedings of the 2023 AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment}}, title = {Story Shaping: Teaching Agents Human-like Behavior with Stories}, year = {2023}, owner = {riedl}, url = {https://arxiv.org/abs/2301.10107}, keywords = {story, int, agents, rl, align}, }

Wei Zhou, Xiangyu Peng, and Mark O. Riedl
Dialogue Shaping: Empowering Agents through NPC Interaction
Proceedings of the 2023 AAAI Workshop on Experimental AI in Games (2023).
arXiv Workshop bibtexInteractive StoriesAgentsReinforcement Learning

@InProceedings{Zhou2023Dialogue, author = {Wei Zhou and Xiangyu Peng and Riedl, Mark O.}, booktitle = {{Proceedings of the 2023 AAAI Workshop on Experimental AI in Games}}, title = {Dialogue Shaping: Empowering Agents through NPC Interaction}, year = {2023}, owner = {riedl}, url = {https://arxiv.org/abs/2307.15833}, keywords = {int, agents, rl}, }

Christopher Cui, Xiangyu Peng, and Mark O. Riedl
Thespian: Multi-Character Text Role-Playing Game Agents
Proceedings of the 2023 AAAI Workshop on Experimental AI in Games (2023).
arXiv Workshop bibtexInteractive StoriesAgentsReinforcement Learning

@InProceedings{Cui2023Thespian, author = {Christopher Cui and Xiangyu Peng and Riedl, Mark O.}, booktitle = {{Proceedings of the 2023 AAAI Workshop on Experimental AI in Games}}, title = {Thespian: Multi-Character Text Role-Playing Game Agents}, year = {2023}, owner = {riedl}, url = {https://arxiv.org/abs/2308.01872}, keywords = {int, agents, rl}, }

Jonathan C. Balloch, Zhiyu Lin, Xiangyu Peng, Mustafa Hussain, Aarun Srinivas, Robert Wright, Julia M. Kim, and Mark O. Riedl
Neuro-Symbolic World Models for Adapting to Open World Novelty
Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (2023).
arXiv Conference bibtexAgentsReinforcement Learning

@inproceedings{Balloch2023WorldModels, author = {Balloch, Jonathan C. and Lin, Zhiyu and Peng, Xiangyu and Hussain, Mustafa and Srinivas, Aarun and Wright, Robert and Kim, Julia M. and Riedl, Mark O.}, title = {Neuro-Symbolic World Models for Adapting to Open World Novelty}, year = {2023}, booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems}, url={https://arxiv.org/abs/2301.06294}, keywords = {agents, rl}, }

2022

Xiangyu Peng, Mark O. Riedl, and Prithviraj Ammanabrolu
Inherently Explainable Reinforcement Learning in Natural Language
Proceedings of NeurIPS 2022 (2022).
arXiv Conference bibtexInteractive StoriesAgentsExplainable AIInterpretabilityReinforcement Learning

@InProceedings{Peng2022InherentlyExplainable, author = {Xiangyu Peng and Riedl, Mark O. and Prithviraj Ammanabrolu}, booktitle = {Proceedings of NeurIPS 2022}, title = {Inherently Explainable Reinforcement Learning in Natural Language}, year = {2022}, owner = {riedl}, url = {https://arxiv.org/abs/2112.08907}, keywords = {int, agents, xai, interp, rl}, }

Jonathan Balloch, Zhiyu Lin, Mustafa Hussain, Aarun Srinivas, Robert Wright, Xiangyu Peng, Julia Kim, and Mark Riedl
NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty
Proceedings of the AAAI Spring Symposium on Designing Artificial Intelligence for Open Worlds (2022).
arXiv Conference bibtexAgentsReinforcement Learning

@InProceedings{Balloch2022NovGrid, author = {Jonathan Balloch and Zhiyu Lin and Mustafa Hussain and Aarun Srinivas and Robert Wright and Xiangyu Peng and Julia Kim and Mark Riedl}, booktitle = {Proceedings of the AAAI Spring Symposium on Designing Artificial Intelligence for Open Worlds}, title = {{NovGrid}: A Flexible Grid World for Evaluating Agent Response to Novelty}, year = {2022}, owner = {riedl}, url = {https://arxiv.org/abs/2203.12117}, keywords = {agents, rl}, }

Jonathan C Balloch, Julia Kim, Jessica L Inman, and Mark O. Riedl
The Role of Exploration for Task Transfer in Reinforcement Learning
Proceedings of the IROS 2022 Workshop on Lifelong Learning of High-level Cognitive and Reasoning Skills (2022).
arXiv Workshop bibtexAgentsReinforcement Learning

@InProceedings{Balloch2022Exploration, author = {Jonathan C Balloch and Julia Kim and Jessica L Inman and Riedl, Mark O.}, booktitle = {Proceedings of the IROS 2022 Workshop on Lifelong Learning of High-level Cognitive and Reasoning Skills}, title = {The Role of Exploration for Task Transfer in Reinforcement Learning}, year = {2022}, owner = {riedl}, url = {https://arxiv.org/abs/2210.06168}, keywords = {agents, rl}, }

Louis Castricato, Alexander Havrilla, Shahbuland Matiana, Michael Pieler, Anbang Ye, Ian Yang, Spencer Frazier, and Mark Riedl
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning
arXiv preprint arXiv:2210.07792 (2022).
arXiv bibtexStorytellingLarge Language ModelsReinforcement LearningValue Alignment

@Article{Castricato2022Robust, author = {Louis Castricato and Alexander Havrilla and Shahbuland Matiana and Michael Pieler and Anbang Ye and Ian Yang and Spencer Frazier and Riedl, Mark}, journal = {arXiv preprint arXiv:2210.07792}, title = {Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning}, year = {2022}, owner = {riedl}, url = {https://arxiv.org/abs/2210.07792}, keywords = {story, llms, rl, align}, }

Xiangyu Peng, Mark O. Riedl, and Prithviraj Ammanabrolu
Inherently Explainable Reinforcement Learning in Natural Language
Proceedings of the 2022 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (2022).
arXiv Conference bibtexInteractive StoriesAgentsExplainable AIInterpretabilityReinforcement Learning

@InProceedings{Peng2022Inherently, author = {Xiangyu Peng and Riedl, Mark O. and Prithviraj Ammanabrolu}, title = {Inherently Explainable Reinforcement Learning in Natural Language}, booktitle = {Proceedings of the 2022 Multi-disciplinary Conference on Reinforcement Learning and Decision Making}, year = {2022}, owner = {riedl}, timestamp = {2022.07.20}, url = {https://arxiv.org/abs/2112.08907}, keywords = {int, agents, xai, interp, rl}, }

Prithviraj Ammanabrolu, Renee Jia, and Mark O. Riedl
Situated Dialogue Learning through Procedural Environment Generation
Proceedings of ACL 2022 (2022).
arXiv Conference bibtexInteractive StoriesAgentsReinforcement LearningGame Generation

@InProceedings{Ammanabrolu2022Situated, author = {Prithviraj Ammanabrolu and Renee Jia and Riedl, Mark O.}, title = {Situated Dialogue Learning through Procedural Environment Generation}, booktitle = {Proceedings of ACL 2022}, year = {2022}, owner = {riedl}, timestamp = {2022.02.24}, url = {https://arxiv.org/abs/2110.03262}, keywords = {int, agents, rl, game}, }

2021

Amal Alabdulkarim, Winston Li, Lara J. Martin, and Mark O. Riedl
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning
arXiv:2112.08593 (2021).
arXiv bibtexStorytellingLarge Language ModelsReinforcement Learning

@Article{Alabdulkarim2021GoalDirected, author = {Amal Alabdulkarim and Winston Li and Martin, Lara J. and Riedl, Mark O.}, title = {Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning}, journal = {arXiv:2112.08593}, year = {2021}, owner = {riedl}, timestamp = {2022.02.24}, url = {https://arxiv.org/abs/2112.08593}, keywords = {story, llms, rl}, }

Prithviraj Ammanabrolu and Mark Riedl
Learning Knowledge Graph-based World Models of Textual Environments
Thirty-fifth Conference on Neural Information Processing Systems (2021).
arXiv Conference bibtexInteractive StoriesAgentsReinforcement Learning

@InProceedings{ammanabrolu2021learning, author = {Prithviraj Ammanabrolu and Mark Riedl}, title = {Learning Knowledge Graph-based World Models of Textual Environments}, booktitle = {Thirty-fifth Conference on Neural Information Processing Systems}, year = {2021}, url = {https://arxiv.org/abs/2106.09608}, keywords = {int, agents, rl}, }

Xiangyu Peng, Prithviraj Ammanabrolu, and Mark Riedl
Explainable Reinforcement Learning Agents with Stacked Hierarchical Graph Attention
Workshop on Explainable Graph-based Machine Learning at AKBC (2021).
Workshop bibtexAgentsExplainable AIInterpretabilityReinforcement Learning

@InProceedings{peng2021explainable, author = {Xiangyu Peng and Prithviraj Ammanabrolu and Mark Riedl}, title = {Explainable Reinforcement Learning Agents with Stacked Hierarchical Graph Attention}, booktitle = {Workshop on Explainable Graph-based Machine Learning at AKBC}, year = {2021}, keywords = {agents, xai, interp, rl}, }

Prithviraj Ammanabrolu and Mark O. Riedl
Modeling Worlds in Text
Proceedings of the 2021 Conference on Neural Information Processing Systems, Benchmarks Track (2021).
arXiv Conference bibtexInteractive StoriesSocial SimulationAgentsReinforcement Learning

@InProceedings{Ammanabrolu2021Modeling, author = {Ammanabrolu, Prithviraj and Riedl, Mark O.}, title = {Modeling Worlds in Text}, booktitle = {Proceedings of the 2021 Conference on Neural Information Processing Systems, Benchmarks Track}, year = {2021}, owner = {riedl}, timestamp = {2021.06.15}, url = {https://arxiv.org/abs/2106.09578}, keywords = {int, worlds, agents, rl}, }

Xiangyu Peng, Jonathan C. Balloch, and Mark O. Riedl
Detecting and Adapting to Novelty in Games
Proceedings of the AAAI21 Workshop on on Reinforcement Learning in Games (2021).
arXiv Workshop bibtexAgentsReinforcement Learning

@InProceedings{Balloch2021Novelty, author = {Xiangyu Peng and Balloch, Jonathan C. and Riedl, Mark O.}, title = {Detecting and Adapting to Novelty in Games}, booktitle = {Proceedings of the AAAI21 Workshop on on Reinforcement Learning in Games}, year = {2021}, owner = {riedl}, timestamp = {2021.06.08}, url = {https://arxiv.org/abs/2106.02204}, keywords = {agents, rl}, }

Md Sultan Al Nahian, Spencer Frazier, Brent Harrison, and Mark O. Riedl
Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior
arXiv:2104.09469 (2021).
arXiv bibtexAgentsReinforcement LearningValue AlignmentAI Safety

@Article{NahianTraining2021, author = {Md Sultan Al Nahian and Spencer Frazier and Brent Harrison and Riedl, Mark O.}, title = {Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior}, journal = {arXiv:2104.09469}, year = {2021}, owner = {riedl}, timestamp = {2021.05.21}, url = {https://arxiv.org/abs/2104.09469}, keywords = {agents, rl, align, safe}, }

D. Srivastava, Spencer Frazier, Mark O. Riedl, and K. Feigh
Effect of Interaction Design of Reinforcement Learning Agents on Human Satisfaction in Partially Observable Domains
International Joint Conference on Computer Vision, Imaging, and Computer Graphics Theory and Applications (2021).
Conference bibtexAgentsExplainable AIReinforcement Learning

@InProceedings{Srivastava2021EffectOI, author = {D. Srivastava and Spencer Frazier and Mark O. Riedl and K. Feigh}, title = {Effect of Interaction Design of Reinforcement Learning Agents on Human Satisfaction in Partially Observable Domains}, booktitle = {International Joint Conference on Computer Vision, Imaging, and Computer Graphics Theory and Applications}, year = {2021}, keywords = {agents, xai, rl}, }

Prithviraj Ammanabrolu and Mark O Riedl
Situated Language Learning via Interactive Narratives
arXiv preprint arXiv:2103.09977 (2021).
arXiv bibtexInteractive StoriesAgentsReinforcement Learning

@Article{ammanabrolu2020situated, author = {Ammanabrolu, Prithviraj and Riedl, Mark O}, title = {Situated Language Learning via Interactive Narratives}, journal = {arXiv preprint arXiv:2103.09977}, year = {2021}, url = {https://arxiv.org/abs/2103.09977}, keywords = {int, agents, rl}, }

2020

Prithviraj Ammanabrolu, Ethan Tien, Zhaochen Luo, and Mark O Riedl
Exploration Strategies for Text-Adventure Agents
Proceedings of the IJCAI Knowledge-based Reinforcement Learning Workshop (2020).
Workshop bibtexInteractive StoriesAgentsReinforcement Learning

@InProceedings{ammanabrolu2020exploration, author = {Ammanabrolu, Prithviraj and Tien, Ethan and Luo, Zhaochen and Riedl, Mark O}, title = {Exploration Strategies for Text-Adventure Agents}, booktitle = {Proceedings of the IJCAI Knowledge-based Reinforcement Learning Workshop}, year = {2020}, keywords = {int, agents, rl}, }

Matthew Hausknecht, Prithviraj Ammanabrolu, Marc-Alexandre Cot'e, and Xingdi Yuan
Interactive Fiction Games: A Colossal Adventure
Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) (2020).
arXiv Conference bibtexInteractive StoriesAgentsReinforcement Learning

@InProceedings{hausknecht19, author = {Matthew Hausknecht and Prithviraj Ammanabrolu and Marc-Alexandre C{\^{o}}t{\'{e}} and Xingdi Yuan}, title = {Interactive Fiction Games: A Colossal Adventure}, booktitle = {Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI)}, year = {2020}, url = {https://arxiv.org/abs/1909.05398}, keywords = {int, agents, rl}, }

Prithviraj Ammanabrolu and Matthew Hausknecht
Graph Constrained Reinforcement Learning for Natural Language Action Spaces
International Conference on Learning Representations (2020).
OpenReview Conference bibtexInteractive StoriesAgentsReinforcement Learning

@InProceedings{ammanabrolu2020Graph, author = {Prithviraj Ammanabrolu and Matthew Hausknecht}, title = {Graph Constrained Reinforcement Learning for Natural Language Action Spaces}, booktitle = {International Conference on Learning Representations}, year = {2020}, url = {https://openreview.net/forum?id=B1x6w0EtwH}, keywords = {int, agents, rl}, }

Prithviraj Ammanabrolu, Ethan Tien, Matthew Hausknecht, and Mark O Riedl
How to avoid being eaten by a grue: Structured exploration strategies for textual worlds
arXiv preprint arXiv:2006.07409 (2020).
arXiv bibtexInteractive StoriesAgentsReinforcement Learning

@Article{ammanabrolu2020avoid, author = {Ammanabrolu, Prithviraj and Tien, Ethan and Hausknecht, Matthew and Riedl, Mark O}, title = {How to avoid being eaten by a grue: Structured exploration strategies for textual worlds}, journal = {arXiv preprint arXiv:2006.07409}, year = {2020}, url = {https://arxiv.org/abs/2006.07409}, keywords = {int, agents, rl}, }

Sahith Dambekodi, Spencer Frazier, Prithviraj Ammanabrolu, and Mark O Riedl
Playing Text-Based Games with Common Sense
Proceedings of the NeurIPS Wordplay workshop (2020).
arXiv Conference bibtexInteractive StoriesAgentsReinforcement Learning

@InProceedings{dambekodi20commonsense, author = {Dambekodi, Sahith and Frazier, Spencer and Ammanabrolu, Prithviraj and Riedl, Mark O}, title = {Playing Text-Based Games with Common Sense}, booktitle = {Proceedings of the NeurIPS Wordplay workshop}, year = {2020}, url = {https://arxiv.org/abs/2012.02757}, keywords = {int, agents, rl}, }

2019

Prithviraj Ammanabrolu and Mark O. Riedl
Transfer in Deep Reinforcement Learning using Knowledge Graphs
Proceedings of the EMNLP Workshop on TextGraphs: Graph-based Methods for Natural Language Processing (2019).
arXiv Workshop bibtexInteractive StoriesAgentsReinforcement Learning

@InProceedings{ammanabrolu:emnlp-textgraphs2019, author = {Ammanabrolu, Prithviraj and Riedl, Mark O.}, title = {Transfer in Deep Reinforcement Learning using Knowledge Graphs}, booktitle = {Proceedings of the EMNLP Workshop on TextGraphs: Graph-based Methods for Natural Language Processing}, year = {2019}, owner = {riedl}, timestamp = {2019.07.16}, url = {https://arxiv.org/abs/1908.06556}, keywords = {int, agents, rl}, }

Pradyumna Tambwekar, Murtaza Dhuliawala, Lara J. Martin, Animesh Mehta, Brent Harrison, and Mark O. Riedl
Controllable Neural Story Plot Generation via Reward Shaping
Proceedings of the 2019 International Joint Conference on Artificial Intelligence (2019).
arXiv Conference bibtexStorytellingReinforcement Learning

@InProceedings{tambwekar:ijcai2019, author = {Pradyumna Tambwekar and Murtaza Dhuliawala and Martin, Lara J. and Animesh Mehta and Brent Harrison and Riedl, Mark O.}, title = {Controllable Neural Story Plot Generation via Reward Shaping}, booktitle = {Proceedings of the 2019 International Joint Conference on Artificial Intelligence}, year = {2019}, owner = {riedl}, timestamp = {2019.07.16}, url = {https://arxiv.org/abs/1809.10736}, keywords = {story, rl}, }

Spencer Frazier and Mark O. Riedl
Improving Deep Reinforcement Learning in Minecraft with Action Advice
Proceedings of the 2019 AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (2019).
arXiv Conference bibtexAgentsReinforcement Learning

@InProceedings{frazier:aiide2019, author = {Frazier, Spencer and Riedl, Mark O.}, title = {Improving Deep Reinforcement Learning in {Minecraft} with Action Advice}, booktitle = {Proceedings of the 2019 AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment}, year = {2019}, owner = {riedl}, timestamp = {2019.07.16}, url = {https://arxiv.org/abs/1908.01007}, keywords = {agents, rl}, }

Prithviraj Ammanabrolu and Mark O. Riedl
Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning
Proceedings of the 2019 NeurIPS Workshop on Word Play: Reinforcement and Language Learning in Text-based Games (2019).
Workshop bibtexInteractive StoriesAgentsReinforcement Learning

@InProceedings{ammanabrolu:neurips-wordplay2019, Title = {Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning}, Author = {Ammanabrolu, Prithviraj and Riedl, Mark O.}, Booktitle = {Proceedings of the 2019 NeurIPS Workshop on Word Play: Reinforcement and Language Learning in Text-based Games}, Year = {2019}, Owner = {riedl}, Timestamp = {2019.01.16}, keywords = {int, agents, rl}, }

Prithviraj Ammanabrolu and Mark O. Riedl
Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning
Proceedings of the 2019 Conference of the North American Association for Computational Linguistics (2019).
arXiv Conference bibtexInteractive StoriesAgentsReinforcement Learning

@InProceedings{Ammanabrolu2019, author = {Ammanabrolu, Prithviraj and Riedl, Mark O.}, title = {Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning}, booktitle = {Proceedings of the 2019 Conference of the North American Association for Computational Linguistics}, year = {2019}, owner = {riedl}, timestamp = {2019.03.01}, url = {https://arxiv.org/abs/1812.01628}, keywords = {int, agents, rl}, }

2018

Lara Martin, Srijan Sood, and Mark Riedl
Dungeons and DQNs: Toward Reinforcement Learning Agents that Play Tabletop Roleplaying Games
Proceedings of the 2018 Joint Workshop on Workshop on Intelligent Cinematography and Editing and Intelligent Narrative Technologies (2018).
PDF Workshop bibtexInteractive StoriesAgentsReinforcement Learning

@InProceedings{martin:int2018, author = {Lara Martin and Srijan Sood and Mark Riedl}, title = {Dungeons and {DQNs}: Toward Reinforcement Learning Agents that Play Tabletop Roleplaying Games}, booktitle = {Proceedings of the 2018 Joint Workshop on Workshop on Intelligent Cinematography and Editing and Intelligent Narrative Technologies}, year = {2018}, owner = {riedl}, timestamp = {2018.11.12}, url = {http://www.cc.gatech.edu/~riedl/pubs/int18.pdf}, keywords = {int, agents, rl}, }

Brent Harrison, Upol Ehsan, and Mark O. Riedl
Guiding Reinforcement Learning Exploration Using Natural Language
Proceedings of the 2018 International Joint Conference on Autonomous Agents and Multi Agent Systems (2018).
arXiv Conference bibtexAgentsReinforcement Learning

@InProceedings{harrison:aamas2018, author = {Brent Harrison and Upol Ehsan and Riedl, Mark O.}, title = {Guiding Reinforcement Learning Exploration Using Natural Language}, booktitle = {Proceedings of the 2018 International Joint Conference on Autonomous Agents and Multi Agent Systems}, year = {2018}, owner = {riedl}, timestamp = {2017.09.12}, url = {https://arxiv.org/abs/1707.08616}, keywords = {agents, rl}, }

2017

Mark O. Riedl and Brent. Harrison
Enter the Matrix: A Virtual World Approach to Safely Interruptable Autonomous Systems
Proceedings of the AAAI 2017 Workshop on SafeAI (2017).
arXiv Workshop bibtexAgentsReinforcement LearningAI Safety

@InProceedings{riedl:arxiv:matrix2017, author = {Riedl, Mark~O. and Harrison, Brent.}, title = {{Enter the Matrix: A Virtual World Approach to Safely Interruptable Autonomous Systems}}, booktitle = {Proceedings of the AAAI 2017 Workshop on SafeAI}, year = {2017}, url = {https://arxiv.org/abs/1703.10284}, keywords = {agents, rl, safe}, }

Z. Lin, B. Harrison, A. Keech, and M. O. Riedl
Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds
ArXiv:1709.03969 (2017).
arXiv bibtexAgentsReinforcement Learning

@Article{lin:arxiv2017, author = {{Lin}, Z. and {Harrison}, B. and {Keech}, A. and {Riedl}, M.~O.}, title = {{Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds}}, journal = {ArXiv:1709.03969}, year = {2017}, url = {https://arxiv.org/abs/1709.03969}, keywords = {agents, rl}, }

Sam Krening, Brent Harrison, Karen Feigh, Charles Isbell, Mark O. Riedl, and Andrea Thomaz
Learning from Explanations using Sentiment and Advice in RL
IEEE Transactions on Cognitive and Developmental Systems (2017).
PDF bibtexAgentsReinforcement Learning

@Article{krening:tcds2017, author = {Sam Krening and Brent Harrison and Karen Feigh and Charles Isbell and Riedl, Mark O. and Andrea Thomaz}, title = {Learning from Explanations using Sentiment and Advice in {RL}}, journal = {IEEE Transactions on Cognitive and Developmental Systems}, year = {2017}, owner = {riedl}, timestamp = {2017.02.03}, url = {http://www.cc.gatech.edu/~riedl/pubs/tcds16.pdf}, keywords = {agents, rl}, }