Author name cluster

Lenz Belzner

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8 papers

2 author rows

ICML Conference 2024 Conference Paper

Detecting Influence Structures in Multi-Agent Reinforcement Learning

Fabian Raoul Pieroth
Katherine E. Fitch
Lenz Belzner

We consider the problem of quantifying the amount of influence one agent can exert on another in the setting of multi-agent reinforcement learning (MARL). As a step towards a unified approach to express agents’ interdependencies, we introduce the total and state influence measurement functions. Both of these are valid for all common MARL systems, such as the discounted reward setting. Additionally, we propose novel quantities, called the total impact measurement (TIM) and state impact measurement (SIM), that characterize one agent’s influence on another by the maximum impact it can have on the other agents’ expected returns and represent instances of impact measurement functions in the average reward setting. Furthermore, we provide approximation algorithms for TIM and SIM with simultaneously learning approximations of agents’ expected returns, error bounds, stability analyses under changes of the policies, and convergence guarantees. The approximation algorithm relies only on observing other agents’ actions and is, other than that, fully decentralized. Through empirical studies, we validate our approach’s effectiveness in identifying intricate influence structures in complex interactions. Our work appears to be the first study of determining influence structures in the multi-agent average reward setting with convergence guarantees.

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AAMAS Conference 2022 Conference Paper

Emergent Cooperation from Mutual Acknowledgment Exchange

Thomy Phan
Felix Sommer
Philipp Altmann
Fabian Ritz
Lenz Belzner
Claudia Linnhoff-Popien

Peer incentivization (PI) is a recent approach, where all agents learn to reward or to penalize each other in a distributed fashion which often leads to emergent cooperation. Current PI mechanisms implicitly assume a flawless communication channel in order to exchange rewards. These rewards are directly integrated into the learning process without any chance to respond with feedback. Furthermore, most PI approaches rely on global information which limits scalability and applicability to real-world scenarios, where only local information is accessible. In this paper, we propose Mutual Acknowledgment Token Exchange (MATE), a PI approach defined by a two-phase communication protocol to mutually exchange acknowledgment tokens to shape individual rewards. Each agent evaluates the monotonic improvement of its individual situation in order to accept or reject acknowledgment requests from other agents. MATE is completely decentralized and only requires local communication and information. We evaluate MATE in three social dilemma domains. Our results show that MATE is able to achieve and maintain significantly higher levels of cooperation than previous PI approaches. In addition, we evaluate the robustness of MATE in more realistic scenarios, where agents can defect from the protocol and where communication failures can occur.

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AAAI Conference 2021 Conference Paper

Resilient Multi-Agent Reinforcement Learning with Adversarial Value Decomposition

Thomy Phan
Lenz Belzner
Thomas Gabor
Andreas Sedlmeier
Fabian Ritz
Claudia Linnhoff-Popien

We focus on resilience in cooperative multi-agent systems, where agents can change their behavior due to udpates or failures of hardware and software components. Current stateof-the-art approaches to cooperative multi-agent reinforcement learning (MARL) have either focused on idealized settings without any changes or on very specialized scenarios, where the number of changing agents is fixed, e. g. , in extreme cases with only one productive agent. Therefore, we propose Resilient Adversarial value Decomposition with Antagonist-Ratios (RADAR). RADAR offers a value decomposition scheme to train competing teams of varying size for improved resilience against arbitrary agent changes. We evaluate RADAR in two cooperative multi-agent domains and show that RADAR achieves better worst case performance w. r. t. arbitrary agent changes than state-of-the-art MARL.

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IJCAI Conference 2021 Conference Paper

Stochastic Market Games

Kyrill Schmid
Lenz Belzner
Robert Müller
Johannes Tochtermann
Claudia Linnhoff-Popien

Some of the most relevant future applications of multi-agent systems like autonomous driving or factories as a service display mixed-motive scenarios, where agents might have conflicting goals. In these settings agents are likely to learn undesirable outcomes in terms of cooperation under independent learning, such as overly greedy behavior. Motivated from real world societies, in this work we propose to utilize market forces to provide incentives for agents to become cooperative. As demonstrated in an iterated version of the Prisoner's Dilemma, the proposed market formulation can change the dynamics of the game to consistently learn cooperative policies. Further we evaluate our approach in spatially and temporally extended settings for varying numbers of agents. We empirically find that the presence of markets can improve both the overall result and agent individual returns via their trading activities.

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NeurIPS Conference 2021 Conference Paper

VAST: Value Function Factorization with Variable Agent Sub-Teams

Thomy Phan
Fabian Ritz
Lenz Belzner
Philipp Altmann
Thomas Gabor
Claudia Linnhoff-Popien

Value function factorization (VFF) is a popular approach to cooperative multi-agent reinforcement learning in order to learn local value functions from global rewards. However, state-of-the-art VFF is limited to a handful of agents in most domains. We hypothesize that this is due to the flat factorization scheme, where the VFF operator becomes a performance bottleneck with an increasing number of agents. Therefore, we propose VFF with variable agent sub-teams (VAST). VAST approximates a factorization for sub-teams which can be defined in an arbitrary way and vary over time, e. g. , to adapt to different situations. The sub-team values are then linearly decomposed for all sub-team members. Thus, VAST can learn on a more focused and compact input representation of the original VFF operator. We evaluate VAST in three multi-agent domains and show that VAST can significantly outperform state-of-the-art VFF, when the number of agents is sufficiently large.

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AAMAS Conference 2019 Conference Paper

Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies

Thomy Phan
Kyrill Schmid
Lenz Belzner
Thomas Gabor
Sebastian Feld
Claudia Linnhoff-Popien

We propose Strong Emergent Policy (STEP) approximation, a scalable approach to learn strong decentralized policies for cooperative MAS with a distributed variant of policy iteration. For that, we use function approximation to learn from action recommendations of a decentralized multi-agent planning algorithm. STEP combines decentralized multi-agent planning with centralized learning, only requiring a generative model for distributed black box optimization. We experimentally evaluate STEP in two challenging and stochastic domains with large state and joint action spaces and show that STEP is able to learn stronger policies than standard multi-agent reinforcement learning algorithms, when combining multi-agent open-loop planning with centralized function approximation. The learned policies can be reintegrated into the multi-agent planning process to further improve performance.

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AAAI Conference 2019 Conference Paper

Memory Bounded Open-Loop Planning in Large POMDPs Using Thompson Sampling

Thomy Phan
Lenz Belzner
Marie Kiermeier
Markus Friedrich
Kyrill Schmid
Claudia Linnhoff-Popien

State-of-the-art approaches to partially observable planning like POMCP are based on stochastic tree search. While these approaches are computationally efficient, they may still construct search trees of considerable size, which could limit the performance due to restricted memory resources. In this paper, we propose Partially Observable Stacked Thompson Sampling (POSTS), a memory bounded approach to openloop planning in large POMDPs, which optimizes a fixed size stack of Thompson Sampling bandits. We empirically evaluate POSTS in four large benchmark problems and compare its performance with different tree-based approaches. We show that POSTS achieves competitive performance compared to tree-based open-loop planning and offers a performancememory tradeoff, making it suitable for partially observable planning with highly restricted computational and memory resources.

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AAMAS Conference 2018 Conference Paper

Leveraging Statistical Multi-Agent Online Planning with Emergent Value Function Approximation

Thomy Phan
Lenz Belzner
Thomas Gabor
Kyrill Schmid

Making decisions is a great challenge in distributed autonomous environments due to enormous state spaces and uncertainty. Many online planning algorithms rely on statistical sampling to avoid searching the whole state space, while still being able to make acceptable decisions. However, planning often has to be performed under strict computational constraints making online planning in multi-agent systems highly limited, which could lead to poor system performance, especially in stochastic domains. In this paper, we propose Emergent Value function Approximation for Distributed Environments (EVADE), an approach to integrate global experience into multi-agent online planning in stochastic domains to consider global effects during local planning. For this purpose, a value function is approximated online based on the emergent system behaviour by using methods of reinforcement learning. We empirically evaluated EVADE with two statistical multi-agent online planning algorithms in a highly complex and stochastic smart factory environment, where multiple agents need to process various items at a shared set of machines. Our experiments show that EVADE can effectively improve the performance of multiagent online planning while offering efficiency w. r. t. the breadth and depth of the planning process.

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