Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning | Paper Citation Record

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This paper cites Deep Reinforcement Learning at the Edge of the Statistical Precipice.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Deep Reinforcement Learning at the Edge of the Statistical Precipice

Reference 1

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This paper cites On the Utility of Learning about Humans for Human-AI Coordination.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning On the Utility of Learning about Humans for Human-AI Coordination

Reference 2

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Unresolved cited work

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This paper cites SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

Reference 4

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This paper cites Approximation capabilities of multilayer feedforward networks.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Approximation capabilities of multilayer feedforward networks

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This paper cites Kingma and Jimmy Ba.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Kingma and Jimmy Ba

Reference 6

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This paper cites On the Variance of the Adaptive Learning Rate and Beyond.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning On the Variance of the Adaptive Learning Rate and Beyond

Reference 7

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This paper cites Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

Reference 8

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This paper cites On Stateful Value Factorization in Multi-Agent Reinforcement Learning.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning On Stateful Value Factorization in Multi-Agent Reinforcement Learning

Reference 9

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Oliehoek and Christopher Amato

Reference 10

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Approximation theory of the MLP model in neural networks

Reference 11

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This paper cites Weighted QMIX: Expand- ing Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Weighted QMIX: Expand- ing Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Reference 12

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This paper cites Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Reference 13

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Observation 9b98e59c-8da1-4d39-9cc6-fadfa1f37fed · outbound

This paper cites JaxMARL: Multi-Agent RL Environments and Algorithms in JAX.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Reference 14

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learn- ing

Reference 15

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This paper cites Value-Decomposition Networks For Cooperative Multi-Agent Learning.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Value-Decomposition Networks For Cooperative Multi-Agent Learning

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning QPLEX: Duplex Dueling Multi-Agent Q-Learning

Reference 17

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This paper cites Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization

Reference 18

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This paper cites strong” forms of UAT not formally applicable, and come to the primary conclusions that (i) only “weak.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning strong” forms of UAT not formally applicable, and come to the primary conclusions that (i) only “weak

Reference 19

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Unresolved cited work

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This paper cites Step 1 was already proven earlier.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Step 1 was already proven earlier

Reference 23

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Unresolved cited work

Reference 24

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Unresolved cited work

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Unresolved cited work

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This paper cites X” over VDN. 0 2 M 4 M 6 M 8 M 10 M Timesteps 0% 25% 50% 75% 100%Pr(X > Q+FIX-sum) Model Q+FIX-sum Q+FIX-mono Q+FIX-lin QPLEX QMIX VDN (b) POI of model “X.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning X” over VDN. 0 2 M 4 M 6 M 8 M 10 M Timesteps 0% 25% 50% 75% 100%Pr(X > Q+FIX-sum) Model Q+FIX-sum Q+FIX-mono Q+FIX-lin QPLEX QMIX VDN (b) POI of model “X

Reference 27

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 28

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Limitations

Reference 29

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This paper cites Proof sketches were omitted in the main document due to the proofs being strictly technical, and space limitations.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Proof sketches were omitted in the main document due to the proofs being strictly technical, and space limitations

Reference 30

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Guidelines: • The answer NA means that the paper does not include experiments

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This paper cites Instructions for the Pymarl2 imple- mentation are provided in the readme.

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Instructions for the Pymarl2 imple- mentation are provided in the readme

Reference 32

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Any additional component (e.g., the architectures of Q+FIX) is both described in the appendix, provided as supplementary material, and will be linked in the camera ready)

Reference 33

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning 30 Guidelines: • The answer NA means that the paper does not include experiments

Reference 34

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Guidelines: • The answer NA means that the paper does not include experiments

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 37

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Guidelines: • The answer NA means that the paper poses no such risks

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Our own implementations continue to use the same license

Reference 39

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning These will be provided as forks from the corresponding repositories

Reference 40

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 41

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 42

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Answer: [NA] Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components

Reference 43

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning Adam: A Method for Stochastic Optimization

Reference 2017

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Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning URL https://proceedings.mlr.press/v97/son19a.html

Reference 5896

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