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Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction

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Mohamed K. Abdelaziz, Mohammed S. Elbamby, Sumudu Samarakoon, Mehdi Bennis

Machine Learning · Submitted 20 Jun 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Simulation results show that the proposed method reaches a better policy, in terms of achievable rewards, resulting in fewer training iterations compared to the case where raw states or fixed state abstraction are used.

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arXiv ID2306.11336 (version 2)
Pith IDZDYBTZVS, Pith's permanent record of this paper and its review
CitationMehdi Bennis, Mohamed K. Abdelaziz, Mohammed S. Elbamby, Sumudu Samarakoon. "Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction." arXiv:2306.11336 (2023). https://pith.science/paper/ZDYBTZVS
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What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.10 · human participants 0.02 · controlled comparison 0.92 · randomized assignment 0.02 · states quantitative result 0.06 · compares with prior results 0.91 · mentions code or data availability 0.02.

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arXiv listing abstract v2. Results and the main claim are underlined; .

Cooperative multi-agent reinforcement learning (MARL) for navigation enables agents to cooperate to achieve their navigation goals. Using emergent communication, agents learn a communication protocol to coordinate and share information that is needed to achieve their navigation tasks. In emergent communication, symbols with no pre-specified usage rules are exchanged, in which the meaning and syntax emerge through training. Learning a navigation policy along with a communication protocol in a MARL environment is highly complex due to the huge state space to be explored. To cope with this complexity, this work proposes a novel neural network architecture, for jointly learning an adaptive state space abstraction and a communication protocol among agents participating in navigation tasks. The goal is to come up with an adaptive abstractor that significantly reduces the size of the state space to be explored, without degradation in the policy performance. Simulation results show that the proposed method reaches a better policy, in terms of achievable rewards, resulting in fewer training iterations compared to the case where raw states or fixed state abstraction are used. Moreover, it is shown that a communication protocol emerges during training which enables the agents to learn better policies within fewer training iterations.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Cooperative multi-agent reinforcement learning (MARL) for navigation enables agents to cooperate to achieve their navigation goals. background 1.00 0.07 0.03 asserted 1.00
s1 Using emergent communication, agents learn a communication protocol to coordinate and share information that is needed to achieve their navigation tasks. background 0.92 0.23 0.05 asserted 1.00
s2 In emergent communication, symbols with no pre-specified usage rules are exchanged, in which the meaning and syntax emerge through training. background 1.00 0.06 0.02 asserted 1.00
s3 Learning a navigation policy along with a communication protocol in a MARL environment is highly complex due to the huge state space to be explored. background 0.87 0.10 0.02 asserted 1.00
s4 To cope with this complexity, this work proposes a novel neural network architecture, for jointly learning an adaptive state space abstraction and a communication protocol among agents participating in navigation tasks. contribution 0.88 0.83 0.09 asserted 0.98
s5 The goal is to come up with an adaptive abstractor that significantly reduces the size of the state space to be explored, without degradation in the policy performance. contribution 0.75 0.23 0.10 asserted 0.56
s6 Simulation results show that the proposed method reaches a better policy, in terms of achievable rewards, resulting in fewer training iterations compared to the case where raw states or fixed state abstraction are used. result 1.00 0.95 0.52 asserted 1.00
s7 Moreover, it is shown that a communication protocol emerges during training which enables the agents to learn better policies within fewer training iterations. result 0.99 0.94 0.23 asserted 1.00

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Cooperative multi-agent reinforcement learning (MARL) for navigation enables agents to cooperate to achieve their navigation goals. Using emergent communication, agents learn a communication protocol to coordinate and share information that is needed to achieve their navigation tasks. In emergent communication, symbols with no pre-specified usage rules are exchanged, in which the meaning and syntax emerge through training. Learning a navigation policy along with a communication protocol in a MARL environment is highly complex due to the huge state space to be explored. To cope with this complexity, this work proposes a novel neural network architecture, for jointly learning an adaptive state space abstraction and a communication protocol among agents participating in navigation tasks. The goal is to come up with an adaptive abstractor that significantly reduces the size of the state space to be explored, without degradation in the policy performance. Simulation results show that the proposed method reaches a better policy, in terms of achievable rewards, resulting in fewer training iterations compared to the case where raw states or fixed state abstraction are used. Moreover, it is shown that a communication protocol emerges during training which enables the agents to learn better policies within fewer training iterations.

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What it cites · 18 sentences without a confident label

Uses the method or tool of 3 papers
  • The reason behind this is to compute a merge and non-merge score for each internal node and sample according to this score by utilizing straight-through Gumbel-softmax estimator (ST-GS) [ref] .

    Categorical Reparameterization with Gumbel-Softmax · MLP and Straight-through Gumbel softmax · 1.00

  • Finally, in order to detect whether communication has emerged at all during the training process, we train the same scenario without a communication channel and compare the achieved performance of both cases, i.e., with and without communication [ref] .

    On the Pitfalls of Measuring Emergent Communication · Emergent communication · 0.87

  • For all simulations, the Adam optimizer [ref] is used with a learning rate of $0.0001$ and parameters $β_1=0.9$, $β_2=0.999$, $ϵ=10^-8$.

    Adam: A Method for Stochastic Optimization · Simulation setup and Analysis · 1.00

Cites as support 1 paper
  • This can be attributed to the structured input space, which allows the agent to see similarities and form concepts, enabling the agent to generalize better [ref] .

    Abstraction for Deep Reinforcement Learning · Generalization · 0.99

Cites as background 3 papers
  • While in [ref] , the authors study the cooperative navigation of multiple agents across photo-realistic environments to reach target locations, where each agent is equipped with a private external memory to persistently store communication information.

    Collaborative Visual Navigation · Introduction · 0.93

  • In [ref] , the authors are interested in understanding how two agents can learn, from pixels, to communicate so as to effectively and collaboratively solve a given task.

    A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks · Introduction · 0.88

  • The choice of $AGGREGATE^(k)$, $COMBINE^(k)$, and $READOUT$ functions in GNNs is crucial [ref] .

    How Powerful are Graph Neural Networks? · Graph Isomorphism Network · 0.80

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