Paper Citation Record · LEDGER

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning

As of 18 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2411.11099.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.11099 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:02:50.937962Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

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verified fuzzy24
unresolved16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 72d251e7-2bb1-4349-9f65-efd38b2e125a · outbound

This paper cites Decentralized multi-agent deep reinforcement learning in swarms of drones for flood monitoring.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Decentralized multi-agent deep reinforcement learning in swarms of drones for flood monitoring

Reference 1

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This paper cites Decentralized control of quadrotor swarms with end-to-end deep reinforcement learning.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Decentralized control of quadrotor swarms with end-to-end deep reinforcement learning

Reference 2

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Observation 3d477659-2268-42e6-9aa9-59a678f415a2 · outbound

This paper cites Opportunities for multiagent systems and multiagent reinforcement learning in traffic control.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

Reference 3

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This paper cites Superhuman ai for heads-up no-limit poker: Libratus beats top professionals.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Superhuman ai for heads-up no-limit poker: Libratus beats top professionals

Reference 4

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This paper cites Deep reinforcement learning in a handful of trials using probabilistic dynamics models.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Deep reinforcement learning in a handful of trials using probabilistic dynamics models

Reference 5

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Observation d05c8313-ede0-4df6-bfe9-d8ed70b62c87 · outbound

This paper cites FACMAC: Factored Multi-Agent Centralised Policy Gradients.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning FACMAC: Factored Multi-Agent Centralised Policy Gradients

Reference 6

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Observation 2bc4d3b8-fecd-41aa-9329-0572a11feec0 · outbound

This paper cites Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks

Reference 7

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Observation 9f2a5d52-d1ab-4360-997b-3474b1b5178b · outbound

This paper cites Learning to communicate with deep multi-agent reinforcement learning.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Learning to communicate with deep multi-agent reinforcement learning

Reference 8

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Observation 253004a3-3f35-48b4-8924-eea47b4436e7 · outbound

This paper cites Addressing function approximation error in actor-critic methods.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Addressing function approximation error in actor-critic methods

Reference 9

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Observation b09c1bca-cda0-4783-bced-1b1e04acfc49 · outbound

This paper cites Uneven: Universal value exploration for multi-agent reinforcement learning.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Uneven: Universal value exploration for multi-agent reinforcement learning

Reference 10

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Observation 66319e40-e65c-4364-aab4-73d23abc5cb6 · outbound

This paper cites A survey of learning in multiagent environments: Dealing with non-stationarity.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning A survey of learning in multiagent environments: Dealing with non-stationarity

Reference 11

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Observation d7e7c90c-d383-4bd5-9e30-fee9876f8e8d · outbound

This paper cites I2q: A fully decentralized q-learning algorithm.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning I2q: A fully decentralized q-learning algorithm

Reference 12

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Observation 5c411b02-7e39-4cb0-bf6b-d0a2aada98d7 · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017

Reference 13

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Observation 12cca604-5929-4333-9beb-2c192a5ffc4d · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 14

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Observation d6d90c6b-4a4d-4bea-9318-a30f84f3b249 · outbound

This paper cites An algorithm for distributed reinforcement learning in cooperative multi-agent systems.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning An algorithm for distributed reinforcement learning in cooperative multi-agent systems

Reference 15

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Observation 21c22cbf-69d6-47ac-b469-763ee94c2cd0 · outbound

This paper cites Solving homogeneous and heterogeneous cooperative tasks with greedy sequential execution.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Solving homogeneous and heterogeneous cooperative tasks with greedy sequential execution

Reference 16

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Observation a850375b-5929-4165-a91b-7639c631c786 · outbound

This paper cites Multi-agent actor-critic for mixed cooperative-competitive environments.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Multi-agent actor-critic for mixed cooperative-competitive environments

Reference 17

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Observation db37c27d-fe6f-4d6b-a197-84f7a2344afc · outbound

This paper cites Hysteretic q-learning: an algorithm for decentralized reinforcement learning in cooperative multi-agent teams.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Hysteretic q-learning: an algorithm for decentralized reinforcement learning in cooperative multi-agent teams

Reference 18

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Observation 7c248329-1e89-43f5-89f0-38ba86288171 · outbound

This paper cites Independent reinforcement learners in cooperative markov games: a survey regarding coordination problems.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Independent reinforcement learners in cooperative markov games: a survey regarding coordination problems

Reference 19

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Observation 701cad7e-da14-4fe5-ac1e-deea4d84aed4 · outbound

This paper cites Deep exploration via bootstrapped dqn.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Deep exploration via bootstrapped dqn

Reference 20

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Observation b301c3ff-e77a-4cbe-bccb-d715a47f91ac · outbound

This paper cites Negative Update Intervals in Deep Multi-Agent Reinforcement Learning.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Negative Update Intervals in Deep Multi-Agent Reinforcement Learning

Reference 21

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Observation e39c91a2-a02c-4551-a808-27884d86b190 · outbound

This paper cites The analysis and design of concurrent learning algorithms for cooperative multiagent systems.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning The analysis and design of concurrent learning algorithms for cooperative multiagent systems

Reference 22

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Observation c73bb3eb-2cf8-4b5e-8a27-9cca2c31ce0e · outbound

This paper cites Biasing coevolutionary search for optimal multiagent behaviors.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Biasing coevolutionary search for optimal multiagent behaviors

Reference 23

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Observation 9dbfb156-17b4-4692-9c2b-732954e7d223 · outbound

This paper cites QMIX : Monotonic value function factorisation for deep multi-agent reinforcement learning.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning QMIX : Monotonic value function factorisation for deep multi-agent reinforcement learning

Reference 24

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Observation 94d80098-1ecc-42c1-8edd-4dbcab3f8bc3 · outbound

This paper cites Weighted qmix: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Weighted qmix: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning

Reference 25

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Observation 5c9c8afc-ee6d-4c4f-8a05-f71ba4094d66 · outbound

This paper cites Monte Carlo statistical methods, volume 2.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Monte Carlo statistical methods, volume 2

Reference 26

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Observation d9f1adbf-ade2-4182-9951-8e1ebbc70782 · outbound

This paper cites Sahraoui, M.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Sahraoui, M

Reference 27

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Observation c766bd39-b8c6-4f60-9cc6-262f82d7d60e · outbound

This paper cites Planning to explore via self-supervised world models.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Planning to explore via self-supervised world models

Reference 28

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b658dd84-9677-499e-bdec-cc87e4438fda · outbound

This paper cites Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving

Reference 29

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Observation e89b844a-6ce9-48c8-89dc-13ba3e2f842b · outbound

This paper cites CURO: Curriculum Learning for Relative Overgeneralization.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning CURO: Curriculum Learning for Relative Overgeneralization

Reference 30

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Observation e58a1369-5a43-4c75-8588-7ed33cf1fc03 · outbound

This paper cites Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning

Reference 31

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Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Lectures on parametric optimization: An introduction

Reference 32

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This paper cites A fully decentralized surrogate for multi-agent policy optimization.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning A fully decentralized surrogate for multi-agent policy optimization

Reference 33

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Observation fa267891-e6d2-41ea-b754-9f44bec6948d · outbound

This paper cites Exploit reward shifting in value-based deep-rl: Optimistic curiosity-based exploration and conservative exploitation via linear reward shaping.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Exploit reward shifting in value-based deep-rl: Optimistic curiosity-based exploration and conservative exploitation via linear reward shaping

Reference 34

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Observation 18c1f776-6e84-4c0c-aa5e-71197a4dd426 · outbound

This paper cites Multi-agent reinforcement learning: Independent vs.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Multi-agent reinforcement learning: Independent vs

Reference 35

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Source-reported events for the cited work

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Observation 23201714-95c8-48f9-b119-170794099c0f · outbound

This paper cites Multi-agent deep reinforcement learning-based trajectory planning for multi-uav assisted mobile edge computing.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Multi-agent deep reinforcement learning-based trajectory planning for multi-uav assisted mobile edge computing

Reference 36

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Observation 9debfa1b-c21c-4a10-ab2f-55d3760cef5a · outbound

This paper cites Lenient learning in independent-learner stochastic cooperative games.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Lenient learning in independent-learner stochastic cooperative games

Reference 37

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Observation 2a0cc246-8bda-41a2-9881-b2d6d38cdd58 · outbound

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Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Multiagent Soft Q-Learning

Reference 38

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Observation 0e30e6ec-ad04-41e5-be26-db7c23e96791 · outbound

This paper cites An analysis of cooperative coevolutionary algorithms.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning An analysis of cooperative coevolutionary algorithms

Reference 39

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Observation c7714f3f-054e-41d6-b19f-15301083ebf4 · outbound

This paper cites Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

Reference 40

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Observation baad5374-9635-4b6f-80d0-27b18ba0bbbc · outbound

This paper cites Learning multi-agent coordination for enhancing target coverage in directional sensor networks.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Learning multi-agent coordination for enhancing target coverage in directional sensor networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:51.220966Z

Source-reported events for the cited work

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Observation 6d5c0fa9-3be5-4485-8e7b-c5b38728aa6b · outbound

This paper cites The surprising effectiveness of ppo in cooperative multi-agent games.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning The surprising effectiveness of ppo in cooperative multi-agent games

Reference 42

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Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Unresolved cited work

Reference 43

Resolution: verified exact; raw_fallback, observed 2026-08-12T19:02:51.131717Z

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Observation 161b8611-60a8-4713-b66f-16235487f7b7 · outbound

This paper cites Smarts: An open-source scalable multi-agent rl training school for autonomous driving.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning Smarts: An open-source scalable multi-agent rl training school for autonomous driving

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:51.207029Z

Source-reported events for the cited work

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Observation 15ce17ea-bca8-44f1-92d6-9bbf30194d75 · outbound

This paper cites A Survey of Multi-Agent Deep Reinforcement Learning with Communication.

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning A Survey of Multi-Agent Deep Reinforcement Learning with Communication

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:50.937962Z

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Pith citing papers

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