| 1 |
Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomes |
AAAI |
2024 |
| 2 |
ASAP: Attention-Based State Space Abstraction for Policy Summarization |
ACML |
2024 |
| 3 |
Causal State Distillation for Explainable Reinforcement Learning |
CLeaR |
2024 |
| 4 |
Discovering Behavioral Modes in Deep Reinforcement Learning Policies Using Trajectory Clustering in Latent Space |
CoRR |
2024 |
| 5 |
Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents |
CoRR |
2024 |
| 6 |
Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning |
CoRR |
2024 |
| 7 |
Detection of Important States through an Iterative Q-value Algorithm for Explainable Reinforcement Learning |
HICSS |
2024 |
| 8 |
''You just can't go around killing people'' Explaining Agent Behavior to a Human Terminator |
ICML Workshop MHFAIA |
2024 |
| 9 |
Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective |
MED |
2024 |
| 10 |
Explaining Deep Reinforcement Learning Policies with SHAP, Decision Trees, and Prototypes |
MED |
2024 |
| 11 |
Local Explanations for Reinforcement Learning |
AAAI |
2023 |
| 12 |
Explainable Reinforcement Learning Based on Q-Value Decomposition by Expected State Transitions |
AAAI-MAKE |
2023 |
| 13 |
GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanations |
AAMAS |
2023 |
| 14 |
Interpreting a deep reinforcement learning model with conceptual embedding and performance analysis |
Appl. Intell. |
2023 |
| 15 |
Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach |
ECML PKDD |
2023 |
| 16 |
Inherently Interpretable Deep Reinforcement Learning Through Online Mimicking |
EXTRAAMAS |
2023 |
| 17 |
A Closer Look at Reward Decomposition for High-Level Robotic Explanations |
ICDL |
2023 |
| 18 |
Towards Interpretable Deep Reinforcement Learning with Human-Friendly Prototypes |
ICLR |
2023 |
| 19 |
Explaining Reinforcement Learning with Shapley Values |
ICML |
2023 |
| 20 |
Counterfactual Explanation Policies in RL |
ICML Workshop on Counterfactuals in Minds and Machines |
2023 |
| 21 |
Explaining Black Box Reinforcement Learning Agents Through Counterfactual Policies |
IDA |
2023 |
| 22 |
Extracting Decision Tree From Trained Deep Reinforcement Learning in Traffic Signal Control |
IEEE Trans. Comput. Soc. Syst. |
2023 |
| 23 |
Real-Time Counterfactual Explanations For Robotic Systems With Multiple Continuous Outputs |
IFAC-PapersOnLine |
2023 |
| 24 |
Explainable Multi-Agent Reinforcement Learning for Temporal Queries |
IJCAI |
2023 |
| 25 |
Explainable Reinforcement Learning via a Causal World Model |
IJCAI |
2023 |
| 26 |
Unveiling Concepts Learned by a World-Class Chess-Playing Agent |
IJCAI |
2023 |
| 27 |
Extracting tactics learned from self-play in general games |
Inf. Sci. |
2023 |
| 28 |
Learning state importance for preference-based reinforcement learning |
Mach. Learn. |
2023 |
| 29 |
Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction |
NeurIPS |
2023 |
| 30 |
State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding |
NeurIPS |
2023 |
| 31 |
StateMask: Explaining Deep Reinforcement Learning through State Mask |
NeurIPS |
2023 |
| 32 |
Comparing explanations in RL |
Neural Comput. Appl. |
2023 |
| 33 |
Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario |
Neural Comput. Appl. |
2023 |
| 34 |
Hierarchical goals contextualize local reward decomposition explanations |
Neural Comput. Appl. |
2023 |
| 35 |
Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization |
Neural Networks |
2023 |
| 36 |
Model tree methods for explaining deep reinforcement learning agents in real-time robotic applications |
Neurocomputing |
2023 |
| 37 |
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents |
PAAMS |
2023 |
| 38 |
Contrastive Visual Explanations for Reinforcement Learning via Counterfactual Rewards |
xAI |
2023 |
| 39 |
IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit Based on Analyses of Interestingness |
xAI |
2023 |
| 40 |
"I Don't Think So": Summarizing Policy Disagreements for Agent Comparison |
AAAI |
2022 |
| 41 |
CAPS: Comprehensible Abstract Policy Summaries for Explaining Reinforcement Learning Agents |
AAMAS |
2022 |
| 42 |
Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions |
AAMAS |
2022 |
| 43 |
Lazy-MDPs: Towards Interpretable RL by Learning When to Act |
AAMAS |
2022 |
| 44 |
Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems |
ACSOS |
2022 |
| 45 |
Analysis of Explainable Goal-Driven Reinforcement Learning in a Continuous Simulated Environment |
Algorithms |
2022 |
| 46 |
BEERL: Both Ends Explanations for Reinforcement Learning |
Applied Sciences |
2022 |
| 47 |
Energy-Efficient Driving for Adaptive Traffic Signal Control Environment via Explainable Reinforcement Learning |
Applied Sciences |
2022 |
| 48 |
Concept Learning for Interpretable Multi-Agent Reinforcement Learning |
CoRL |
2022 |
| 49 |
Comparing Strategies for Visualizing the High-Dimensional Exploration Behavior of CPS Design Agents |
DESTION |
2022 |
| 50 |
InAction: Interpretable Action Decision Making for Autonomous Driving |
ECCV |
2022 |
| 51 |
Enhanced Oblique Decision Tree Enabled Policy Extraction for Deep Reinforcement Learning in Power System Emergency Control |
Electric Power Systems Research |
2022 |
| 52 |
Attributation Analysis of Reinforcement Learning-Based Highway Driver |
Electronics |
2022 |
| 53 |
Multi-objective Genetic Programming for Explainable Reinforcement Learning |
EuroGP |
2022 |
| 54 |
Deep-Learning-based Fuzzy Symbolic Processing with Agents Capable of Knowledge Communication |
ICAART |
2022 |
| 55 |
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations |
ICLR |
2022 |
| 56 |
POETREE: Interpretable Policy Learning with Adaptive Decision Trees |
ICLR |
2022 |
| 57 |
Programmatic Reinforcement Learning without Oracles |
ICLR |
2022 |
| 58 |
Explaining Reinforcement Learning Policies through Counterfactual Trajectories |
ICML Workshop on HILL |
2022 |
| 59 |
Mean-variance Based Risk-sensitive Reinforcement Learning with Interpretable Attention |
ICMVA |
2022 |
| 60 |
Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning |
ICPR |
2022 |
| 61 |
Explaining Intelligent Agent's Future Motion on Basis of Vocabulary Learning With Human Goal Inference |
IEEE Access |
2022 |
| 62 |
Interpretable Autonomous Flight Via Compact Visualizable Neural Circuit Policies |
IEEE Robotics Autom. Lett. |
2022 |
| 63 |
Explainable AI in Deep Reinforcement Learning Models for Power System Emergency Control |
IEEE Trans. Comput. Soc. Syst. |
2022 |
| 64 |
Hierarchical Program-Triggered Reinforcement Learning Agents for Automated Driving |
IEEE Trans. Intell. Transp. Syst. |
2022 |
| 65 |
Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning |
IEEE Trans. Intell. Transp. Syst. |
2022 |
| 66 |
Continuous Action Reinforcement Learning From a Mixture of Interpretable Experts |
IEEE Trans. Pattern Anal. Mach. Intell. |
2022 |
| 67 |
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning |
IEEE Trans. Pattern Anal. Mach. Intell. |
2022 |
| 68 |
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning |
IEEE Trans. Pattern Anal. Mach. Intell. |
2022 |
| 69 |
Visual Analytics for RNN-Based Deep Reinforcement Learning |
IEEE Trans. Vis. Comput. Graph. |
2022 |
| 70 |
Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems |
IEEE Transactions on Cybernetics |
2022 |
| 71 |
Understanding via Exploration: Discovery of Interpretable Features With Deep Reinforcement Learning |
IEEE Transactions on Neural Networks and Learning Systems |
2022 |
| 72 |
Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction |
IJCAI Workshop on XAI |
2022 |
| 73 |
ACMViz: a visual analytics approach to understand DRL-based autonomous control model |
J. Vis. |
2022 |
| 74 |
Incorporating Explanations to Balance the Exploration and Exploitation of Deep Reinforcement Learning |
KSEM |
2022 |
| 75 |
Towards Explainable Reinforcement Learning Using Scoring Mechanism Augmented Agents |
KSEM |
2022 |
| 76 |
Explainable Reinforcement Learning via Model Transforms |
NeurIPS |
2022 |
| 77 |
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis |
NeurIPS |
2022 |
| 78 |
Inherently Explainable Reinforcement Learning in Natural Language |
NeurIPS |
2022 |
| 79 |
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning |
NeurIPS |
2022 |
| 80 |
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping |
NeurIPS |
2022 |
| 81 |
(When) Are Contrastive Explanations of Reinforcement Learning Helpful? |
NeurIPS workshop on HiLL |
2022 |
| 82 |
Mo"ET: Mixture of Expert Trees and its application to verifiable reinforcement learning |
Neural Networks |
2022 |
| 83 |
Analysing deep reinforcement learning agents trained with domain randomisation |
Neurocomputing |
2022 |
| 84 |
Why? Why not? When? Visual Explanations of Agent Behaviour in Reinforcement Learning |
PacificVis |
2022 |
| 85 |
Driving behavior explanation with multi-level fusion |
Pattern Recognit. |
2022 |
| 86 |
Acquisition of chess knowledge in AlphaZero |
Proc. Natl. Acad. Sci. U.S.A. |
2022 |
| 87 |
Learning Interpretable, High-Performing Policies for Autonomous Driving |
Robotics: Science and Systems |
2022 |
| 88 |
Event-driven temporal models for explanations - ETeMoX: explaining reinforcement learning |
Softw. Syst. Model. |
2022 |
| 89 |
Toward a Psychology of Deep Reinforcement Learning Agents Using a Cognitive Architecture |
Top. Cogn. Sci. |
2022 |
| 90 |
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning |
AAAI |
2021 |
| 91 |
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods |
AAAI |
2021 |
| 92 |
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments |
AAAI |
2021 |
| 93 |
Explaining Deep Reinforcement Learning Agents in the Atari Domain through a Surrogate Model |
AIIDE |
2021 |
| 94 |
A framework of explanation generation toward reliable autonomous robots |
Adv. Robotics |
2021 |
| 95 |
Explainable Deep Reinforcement Learning for UAV autonomous path planning |
Aerospace Science and Technology |
2021 |
| 96 |
Explaining robot policies |
Applied AI Letters |
2021 |
| 97 |
Counterfactual state explanations for reinforcement learning agents via generative deep learning |
Artif. Intell. |
2021 |
| 98 |
Local and global explanations of agent behavior: Integrating strategy summaries with saliency maps |
Artif. Intell. |
2021 |
| 99 |
XPM: An Explainable Deep Reinforcement Learning Framework for Portfolio Management |
CIKM |
2021 |
| 100 |
Interactive Explanations: Diagnosis and Repair of Reinforcement Learning Based Agent Behaviors |
CoG |
2021 |
| 101 |
CDT: Cascading Decision Trees for Explainable Reinforcement Learning |
CoRR |
2021 |
| 102 |
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents |
CoRR |
2021 |
| 103 |
Approximating a deep reinforcement learning docking agent using linear model trees |
ECC |
2021 |
| 104 |
Robotic Lever Manipulation using Hindsight Experience Replay and Shapley Additive Explanations |
ECC |
2021 |
| 105 |
Off-Policy Differentiable Logic Reinforcement Learning |
ECML PKDD |
2021 |
| 106 |
Neuro-Symbolic Reinforcement Learning with First-Order Logic |
EMNLP |
2021 |
| 107 |
Explainable Reinforcement Learning for Longitudinal Control |
ICAART |
2021 |
| 108 |
Explainable deep reinforcement learning for portfolio management: an empirical approach |
ICAIF |
2021 |
| 109 |
Explainable Reinforcement Learning for Human-Robot Collaboration |
ICAR |
2021 |
| 110 |
DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation |
ICCV |
2021 |
| 111 |
Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions |
ICLR |
2021 |
| 112 |
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning |
ICLR |
2021 |
| 113 |
Learning "What-if" Explanations for Sequential Decision-Making |
ICLR |
2021 |
| 114 |
Discovering symbolic policies with deep reinforcement learning |
ICML |
2021 |
| 115 |
Re-understanding Finite-State Representations of Recurrent Policy Networks |
ICML |
2021 |
| 116 |
Explainable Reinforcement Learning with the Tsetlin Machine |
IEA/AIE |
2021 |
| 117 |
A Blood Glucose Control Framework Based on Reinforcement Learning With Safety and Interpretability: In Silico Validation |
IEEE Access |
2021 |
| 118 |
Symbolic Regression Methods for Reinforcement Learning |
IEEE Access |
2021 |
| 119 |
Efficient Robotic Object Search Via HIEM: Hierarchical Policy Learning With Intrinsic-Extrinsic Modeling |
IEEE Robotics Autom. Lett. |
2021 |
| 120 |
Learning to Discover Task-Relevant Features for Interpretable Reinforcement Learning |
IEEE Robotics Autom. Lett. |
2021 |
| 121 |
Explaining Deep Learning Models Through Rule-Based Approximation and Visualization |
IEEE Trans. Fuzzy Syst. |
2021 |
| 122 |
Interpretable Decision-Making for Autonomous Vehicles at Highway On-Ramps With Latent Space Reinforcement Learning |
IEEE Trans. Veh. Technol. |
2021 |
| 123 |
Explainable AI methods on a deep reinforcement learning agent for automatic docking |
IFAC-PapersOnLine |
2021 |
| 124 |
Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learning |
IJCNN |
2021 |
| 125 |
Programmatic Policy Extraction by Iterative Local Search |
ILP |
2021 |
| 126 |
Explaining the Decisions of Deep Policy Networks for Robotic Manipulations |
IROS |
2021 |
| 127 |
XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees |
IROS |
2021 |
| 128 |
Mixed Autonomous Supervision in Traffic Signal Control |
ITSC |
2021 |
| 129 |
Can You Trust Your Autonomous Car? Interpretable and Verifiably Safe Reinforcement Learning |
IV |
2021 |
| 130 |
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization |
Journal of Marine Science and Engineering |
2021 |
| 131 |
Visual Analysis of Deep Q-network |
KSII Trans. Internet Inf. Syst. |
2021 |
| 132 |
Automatic discovery of interpretable planning strategies |
Mach. Learn. |
2021 |
| 133 |
EDGE: Explaining Deep Reinforcement Learning Policies |
NeurIPS |
2021 |
| 134 |
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning |
NeurIPS |
2021 |
| 135 |
Learning to Synthesize Programs as Interpretable and Generalizable Policies |
NeurIPS |
2021 |
| 136 |
Machine versus Human Attention in Deep Reinforcement Learning Tasks |
NeurIPS |
2021 |
| 137 |
Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems |
NeurIPS Workshop on Deep RL |
2021 |
| 138 |
Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment |
NeurIPS Workshop on Machine Learning for Health |
2021 |
| 139 |
Feature-Based Interpretable Reinforcement Learning based on State-Transition Models |
SMC |
2021 |
| 140 |
A co-evolutionary approach to interpretable reinforcement learning in environments with continuous action spaces |
SSCI |
2021 |
| 141 |
Interpretable AI Agent Through Nonlinear Decision Trees for Lane Change Problem |
SSCI |
2021 |
| 142 |
Learning Sparse Evidence- Driven Interpretation to Understand Deep Reinforcement Learning Agents |
SSCI |
2021 |
| 143 |
Explainable Reinforcement Learning through a Causal Lens |
AAAI |
2020 |
| 144 |
Attribution-based Salience Method towards Interpretable Reinforcement Learning |
AAAI-MAKE |
2020 |
| 145 |
Learning an Interpretable Traffic Signal Control Policy |
AAMAS |
2020 |
| 146 |
Optimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning |
AISTATS |
2020 |
| 147 |
Interestingness elements for explainable reinforcement learning: Understanding agents' capabilities and limitations |
Artif. Intell. |
2020 |
| 148 |
Model primitives for hierarchical lifelong reinforcement learning |
Auton. Agents Multi Agent Syst. |
2020 |
| 149 |
Understanding the Behavior of Reinforcement Learning Agents |
BIOMA |
2020 |
| 150 |
Methodology for Interpretable Reinforcement Learning Model for HVAC Energy Control |
Big Data |
2020 |
| 151 |
Explaining Autonomous Driving by Learning End-to-End Visual Attention |
CVPRW |
2020 |
| 152 |
Understanding Learned Reward Functions |
CoRR |
2020 |
| 153 |
Interpretable policy derivation for reinforcement learning based on evolutionary feature synthesis |
Complex & Intelligent Systems |
2020 |
| 154 |
DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning |
Comput. Graph. Forum |
2020 |
| 155 |
Understanding RL Vision |
Distill |
2020 |
| 156 |
Interpretable policies for reinforcement learning by empirical fuzzy sets |
Eng. Appl. Artif. Intell. |
2020 |
| 157 |
Neuroevolution of self-interpretable agents |
GECCO |
2020 |
| 158 |
Topological Visualization Method for Understanding the Landscape of Value Functions and Structure of the State Space in Reinforcement Learning |
ICAART |
2020 |
| 159 |
Identifying Critical States by the Action-Based Variance of Expected Return |
ICANN |
2020 |
| 160 |
TLdR: Policy Summarization for Factored SSP Problems Using Temporal Abstractions |
ICAPS |
2020 |
| 161 |
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution |
ICLR |
2020 |
| 162 |
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning |
ICLR |
2020 |
| 163 |
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents |
ICLR |
2020 |
| 164 |
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions |
ICML |
2020 |
| 165 |
Deep Reinforcement Learning for Safe Local Planning of a Ground Vehicle in Unknown Rough Terrain |
IEEE Robotics Autom. Lett. |
2020 |
| 166 |
Towards Interpretable Reinforcement Learning with State Abstraction Driven by External Knowledge |
IEICE Trans. Inf. Syst. |
2020 |
| 167 |
Improved Policy Extraction via Online Q-Value Distillation |
IJCNN |
2020 |
| 168 |
Visualization of topographical internal representation of learning robots |
IJCNN |
2020 |
| 169 |
Explainable navigation system using fuzzy reinforcement learning |
IJIDeM |
2020 |
| 170 |
Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case |
ITSC |
2020 |
| 171 |
xGAIL: Explainable Generative Adversarial Imitation Learning for Explainable Human Decision Analysis |
KDD |
2020 |
| 172 |
What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes |
NeurIPS |
2020 |
| 173 |
Explaining Conditions for Reinforcement Learning Behaviors from Real and Imagined Data |
NeurIPS Workshop on Challenges of Real-World RL |
2020 |
| 174 |
DynamicsExplorer: Visual Analytics for Robot Control Tasks involving Dynamics and LSTM-based Control Policies |
PacificVis |
2020 |
| 175 |
Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving |
Robotics Auton. Syst. |
2020 |
| 176 |
Modelling Agent Policies with Interpretable Imitation Learning |
TAILOR |
2020 |
| 177 |
Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis |
xxAI - Beyond Explainable AI |
2020 |
| 178 |
Generation of Policy-Level Explanations for Reinforcement Learning |
AAAI |
2019 |
| 179 |
SDRL: Interpretable and Data-Efficient Deep Reinforcement Learning Leveraging Symbolic Planning |
AAAI |
2019 |
| 180 |
Towards Better Interpretability in Deep Q-Networks |
AAAI |
2019 |
| 181 |
Toward Robust Policy Summarization |
AAMAS |
2019 |
| 182 |
Towards Governing Agent's Efficacy: Action-Conditional \textdollar(\beta)\textdollar-VAE for Deep Transparent Reinforcement Learning |
ACML |
2019 |
| 183 |
Memory-Based Explainable Reinforcement Learning |
AI |
2019 |
| 184 |
Summarizing agent strategies |
Auton. Agents Multi Agent Syst. |
2019 |
| 185 |
Enabling robots to communicate their objectives |
Auton. Robots |
2019 |
| 186 |
Visualization of Deep Reinforcement Learning using Grad-CAM: How AI Plays Atari Games? |
CoG |
2019 |
| 187 |
Explaining Reward Functions in Markov Decision Processes |
FLAIRS |
2019 |
| 188 |
Explanation-Based Reward Coaching to Improve Human Performance via Reinforcement Learning |
HRI |
2019 |
| 189 |
Free-Lunch Saliency via Attention in Atari Agents |
ICCVW |
2019 |
| 190 |
Deep reinforcement learning with relational inductive biases |
ICLR |
2019 |
| 191 |
Learning Finite State Representations of Recurrent Policy Networks |
ICLR |
2019 |
| 192 |
Neural Logic Reinforcement Learning |
ICML |
2019 |
| 193 |
Interpretable Approximation of a Deep Reinforcement Learning Agent as a Set of If-Then Rules |
ICMLA |
2019 |
| 194 |
Semantic Predictive Control for Explainable and Efficient Policy Learning |
ICRA |
2019 |
| 195 |
DQNViz: A Visual Analytics Approach to Understand Deep Q-Networks |
IEEE Trans. Vis. Comput. Graph. |
2019 |
| 196 |
Visualizing Deep Q-Learning to Understanding Behavior of Swarm Robotic System |
IES |
2019 |
| 197 |
Exploring Computational User Models for Agent Policy Summarization |
IJCA |
2019 |
| 198 |
Explaining Reinforcement Learning to Mere Mortals: An Empirical Study |
IJCAI |
2019 |
| 199 |
Counterfactual States for Atari Agents via Generative Deep Learning |
IJCAI Workshop on XAI |
2019 |
| 200 |
Distilling Deep Reinforcement Learning Policies in Soft Decision Trees |
IJCAI Workshop on XAI |
2019 |
| 201 |
Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation |
IROS |
2019 |
| 202 |
Reinforcement Learning with Explainability for Traffic Signal Control |
ITSC |
2019 |
| 203 |
Interestingness Elements for Explainable Reinforcement Learning through Introspection |
IUI Workshops |
2019 |
| 204 |
Explainable Reinforcement Learning via Reward Decomposition |
JCAI Workshop on XAI |
2019 |
| 205 |
Enhancing Explainability of Deep Reinforcement Learning Through Selective Layer-Wise Relevance Propagation |
KI |
2019 |
| 206 |
Imitation-Projected Programmatic Reinforcement Learning |
NeurIPS |
2019 |
| 207 |
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents |
NeurIPS |
2019 |
| 208 |
Verbal Explanations for Deep Reinforcement Learning Neural Networks with Attention on Extracted Features |
RO-MAN |
2019 |
| 209 |
A formal methods approach to interpretable reinforcement learning for robotic planning |
Sci. Robotics |
2019 |
| 210 |
HIGHLIGHTS: Summarizing Agent Behavior to People |
AAMAS |
2018 |
| 211 |
Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations |
AIES |
2018 |
| 212 |
Transparency and Explanation in Deep Reinforcement Learning Neural Networks |
AIES |
2018 |
| 213 |
Visual Rationalizations in Deep Reinforcement Learning for Atari Games |
BNAIC |
2018 |
| 214 |
Textual Explanations for Self-Driving Vehicles |
ECCV |
2018 |
| 215 |
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees |
ECML PKDD |
2018 |
| 216 |
Interpretable policies for reinforcement learning by genetic programming |
Eng. Appl. Artif. Intell. |
2018 |
| 217 |
Generating interpretable fuzzy controllers using particle swarm optimization and genetic programming |
GECCO |
2018 |
| 218 |
Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning |
ICLR |
2018 |
| 219 |
Programmatically Interpretable Reinforcement Learning |
ICML |
2018 |
| 220 |
Visualizing and Understanding Atari Agents |
ICML |
2018 |
| 221 |
Deep Reinforcement Learning Monitor for Snapshot Recording |
ICMLA |
2018 |
| 222 |
Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences |
IJCAI Workshop on XAI |
2018 |
| 223 |
Explaining Deep Adaptive Programs via Reward Decomposition |
IJCAI/ECAI Workshop XAI |
2018 |
| 224 |
Establishing Appropriate Trust via Critical States |
IROS |
2018 |
| 225 |
Unsupervised Video Object Segmentation for Deep Reinforcement Learning |
NeurIPS |
2018 |
| 226 |
Verifiable Reinforcement Learning via Policy Extraction |
NeurIPS |
2018 |
| 227 |
Visual Sparse Bayesian Reinforcement Learning: A Framework for Interpreting What an Agent Has Learned |
SSCI |
2018 |
| 228 |
Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies |
Eng. Appl. Artif. Intell. |
2017 |
| 229 |
Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents |
HAI |
2017 |
| 230 |
Improving Robot Controller Transparency Through Autonomous Policy Explanation |
HRI |
2017 |
| 231 |
Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention |
ICCV |
2017 |
| 232 |
Application of Instruction-Based Behavior Explanation to a Reinforcement Learning Agent with Changing Policy |
ICONIP |
2017 |
| 233 |
Graying the black box: Understanding DQNs |
ICML |
2016 |