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arxiv: 1907.03053 · v1 · pith:MRQ3VIWNnew · submitted 2019-07-06 · 💻 cs.LG · cs.MA· stat.ML

A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning

Yixuan Lin , Kaiqing Zhang , Zhuoran Yang , Zhaoran Wang , Tamer Ba\c{s}ar , Romeil Sandhu , Ji Liu This is my paper

Pith reviewed 2026-05-25 01:48 UTC · model grok-4.3

classification 💻 cs.LG cs.MAstat.ML
keywords distributed reinforcement learningmulti-agent actor-criticcommunication efficiencydirected graphscooperative policy learningstrongly connected networks
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The pith

A multi-agent actor-critic algorithm solves distributed reinforcement learning on directed graphs by transmitting only two scalars per step.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Agents connected over a directed graph aim to cooperatively maximize the average return of a shared policy by exchanging information only with immediate neighbors. The paper introduces a randomized multi-agent actor-critic method that performs local policy and value updates while restricting each transmission to two scalar values. The central result states that this procedure reaches the globally optimal solution whenever the graph is strongly connected. This matters for settings where agents must learn together yet cannot afford to share full trajectories or parameter vectors at every round.

Core claim

The authors present a randomized communication-efficient multi-agent actor-critic algorithm that enables a network of agents to maximize the globally averaged return through local communications on a directed graph. They demonstrate that the algorithm solves the distributed reinforcement learning problem for strongly connected graphs with each agent transmitting only two scalar-valued variables at each time step.

What carries the argument

randomized communication-efficient multi-agent actor-critic algorithm that performs local actor and critic updates while exchanging only two scalar values per transmission to maintain consensus across the directed graph.

If this is right

  • Each agent needs to send only two scalar values at each communication round while still achieving global optimality.
  • The method works for unidirectional communication links represented by a directed graph.
  • Convergence holds as long as the graph remains strongly connected.
  • The approach applies to any finite set of agents that can exchange messages with their out-neighbors.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The two-scalar restriction may allow the same structure to be used in other distributed policy optimization settings that require consensus.
  • Randomization in the communication schedule could be examined for its effect on convergence speed when message delays occur.
  • The minimal communication load suggests the algorithm could run on hardware with strict bandwidth limits, such as embedded multi-robot teams.

Load-bearing premise

The underlying communication network forms a strongly connected directed graph.

What would settle it

Execute the algorithm on a directed graph that is not strongly connected and check whether the agents still converge to the policy that maximizes the globally averaged return.

read the original abstract

This paper considers a distributed reinforcement learning problem in which a network of multiple agents aim to cooperatively maximize the globally averaged return through communication with only local neighbors. A randomized communication-efficient multi-agent actor-critic algorithm is proposed for possibly unidirectional communication relationships depicted by a directed graph. It is shown that the algorithm can solve the problem for strongly connected graphs by allowing each agent to transmit only two scalar-valued variables at one time.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The manuscript proposes a randomized multi-agent actor-critic algorithm for distributed reinforcement learning in which agents connected by a directed graph cooperatively maximize the globally averaged return. The central claim is that the algorithm solves this problem on strongly connected graphs while requiring each agent to transmit only two scalar-valued variables per time step.

Significance. If the convergence result holds under the stated graph condition, the work would contribute a communication-efficient protocol for distributed RL by adapting standard push-sum consensus techniques to the actor-critic setting. This addresses a practical bottleneck in multi-agent systems with unidirectional links and provides a concrete performance guarantee tied to strong connectivity.

minor comments (2)
  1. The abstract asserts that the algorithm 'solves the problem' but does not list the precise assumptions (e.g., on step-size schedules, reward boundedness, or function approximation) required for the claimed convergence; these should be stated explicitly in the introduction or theorem statements.
  2. Notation for the two transmitted scalars and the push-sum style updates should be introduced with a clear table or diagram in the algorithm section to improve readability for readers unfamiliar with consensus methods.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the careful reading and positive evaluation of our manuscript. The referee's summary correctly identifies the key contribution: a randomized multi-agent actor-critic method that achieves convergence on strongly connected directed graphs while transmitting only two scalars per agent per step. We are pleased that the work is viewed as addressing a practical bottleneck in multi-agent RL with unidirectional communication.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces a new randomized multi-agent actor-critic algorithm for distributed RL and proves it solves the cooperative maximization problem on strongly connected directed graphs while transmitting only two scalars per step. This is a constructive algorithmic contribution whose central result follows from the algorithm definition plus standard consensus analysis under the explicit graph assumption; no step reduces a claimed prediction or uniqueness result to a fitted input, self-citation chain, or definitional tautology. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract provides no explicit free parameters, invented entities, or non-standard axioms; standard MDP assumptions are implicitly required but not detailed.

axioms (1)
  • domain assumption The underlying environment is a Markov decision process.
    Standard background assumption for any actor-critic RL paper; invoked implicitly by the problem statement.

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discussion (0)

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