Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning | Paper Citation Record

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This paper cites AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers

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This paper cites Goodfellow, Moritz Hardt, and Been Kim.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Goodfellow, Moritz Hardt, and Been Kim

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This paper cites Understanding intermediate layers using linear classifier probes.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Understanding intermediate layers using linear classifier probes

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This paper cites Evaluating Post-hoc Interpretability with Intrinsic Interpretability.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Evaluating Post-hoc Interpretability with Intrinsic Interpretability

Reference 7

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This paper cites Eliciting Latent Predictions from Transformers with the Tuned Lens.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Eliciting Latent Predictions from Transformers with the Tuned Lens

Reference 11

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This paper cites LEACE: Perfect linear concept erasure in closed form.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning LEACE: Perfect linear concept erasure in closed form

Reference 12

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Reference 14

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This paper cites An Interpretability Illusion for BERT.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning An Interpretability Illusion for BERT

Reference 15

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This paper cites Genie: Generative Interactive Environments.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Genie: Generative Interactive Environments

Reference 16

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This paper cites Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Small.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Small

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This paper cites Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective

Reference 19

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This paper cites Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing

Reference 21

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This paper cites Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning

Reference 22

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This paper cites Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning

Reference 23

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This paper cites Predicting Future Actions of Reinforcement Learning Agents.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Predicting Future Actions of Reinforcement Learning Agents

Reference 24

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This paper cites Gradient Routing: Masking Gradients to Localize Computation in Neural Networks.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Gradient Routing: Masking Gradients to Localize Computation in Neural Networks

Reference 25

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This paper cites Towards Automated Circuit Discovery for Mechanistic Interpretability.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 26

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This paper cites Lundberg, and Su-In Lee.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Lundberg, and Su-In Lee

Reference 27

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Reference 28

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This paper cites Anders, Wojciech Samek, and Sebastian Lapuschkin.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Anders, Wojciech Samek, and Sebastian Lapuschkin

Reference 30

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This paper cites Transcoders Find Interpretable LLM Feature Circuits.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Transcoders Find Interpretable LLM Feature Circuits

Reference 31

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This paper cites Interpretability Illusions in the Generalization of Simplified Models.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Interpretability Illusions in the Generalization of Simplified Models

Reference 33

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This paper cites Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning

Reference 34

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Visualizing and Understanding Atari Agents

Reference 37

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Gupta, Maxim Egorov, and Mykel J

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This paper cites Mastering Diverse Domains through World Models.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Mastering Diverse Domains through World Models

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This paper cites Information based explanation methods for deep learning agents -- with applications on large open-source chess models.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Information based explanation methods for deep learning agents -- with applications on large open-source chess models

Reference 42

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning LLM Multi-Agent Systems: Challenges and Open Problems

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Zhang, Shaoqing Ren, and Jian Sun

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond

Reference 45

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Structured World Representations in Maze-Solving Transformers

Reference 52

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio García Castañeda, Charlie Beattie, Neil C

Reference 53

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations

Reference 55

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Backward Lens: Projecting Language Model Gradients into the Vocabulary Space

Reference 58

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This paper cites Evidence of Learned Look-Ahead in a Chess-Playing Neural Network.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Evidence of Learned Look-Ahead in a Chess-Playing Neural Network

Reference 59

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This paper cites Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV).

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

Reference 60

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop

Reference 61

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This paper cites AtP*: An efficient and scalable method for localizing LLM behaviour to components.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning AtP*: An efficient and scalable method for localizing LLM behaviour to components

Reference 62

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This paper cites On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL

Reference 64

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Reference 65

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Renard, and Marcin Detyniecki

Reference 66

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Clustered Policy Decision Ranking

Reference 67

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Unresolved cited work

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Engelhardt, Wolfgang Konen, and Laurenz Wiskott

Reference 69

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks

Reference 70

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Lundberg and Su-In Lee

Reference 71

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Interpretability Needs a New Paradigm

Reference 72

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Reference 73

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Unresolved cited work

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

Reference 76

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Unresolved cited work

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Unresolved cited work

Reference 78

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Unresolved cited work

Reference 79

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Kamhoua, Evangelos E

Reference 80

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Efficiently Quantifying Individual Agent Importance in Cooperative MARL

Reference 81

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning

Reference 82

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Explaining NonLinear Classification Decisions with Deep Taylor Decomposition

Reference 83

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Unresolved cited work

Reference 84

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Unresolved cited work

Reference 85

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning In-context Learning and Induction Heads

Reference 86

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Understanding and Controlling a Maze-Solving Policy Network

Reference 87

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Intent-aligned AI systems deplete human agency: the need for agency foundations research in AI safety

Reference 88

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Unresolved cited work

Reference 89

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Dissecting Language Models: Machine Unlearning via Selective Pruning

Reference 90

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Progress measures for grokking via mechanistic interpretability

Reference 91

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Contrastive Sparse Autoencoders for Interpreting Planning of Chess-Playing Agents

Reference 92

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Observation 75c9485e-04aa-4eee-ab1e-b330b4ecfb1c · outbound

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges

Reference 93

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Unresolved cited work

Reference 94

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Unresolved cited work

Reference 95

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Unresolved cited work

Reference 96

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning ProtoX: Explaining a Reinforcement Learning Agent via Prototyping

Reference 97

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Unresolved cited work

Reference 98

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning A Generalist Agent

Reference 99

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Unresolved cited work

Reference 100

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Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Understanding Addition in Transformers

Reference 101

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