Discovering Causality for Efficient Cooperation in Multi-Agent Environments

• URL: http://arxiv.org/abs/2306.11846v1
• Date: Tue, 20 Jun 2023 18:56:25 GMT
• Title: Discovering Causality for Efficient Cooperation in Multi-Agent Environments
• Authors: Rafael Pina, Varuna De Silva, Corentin Artaud
• Abstract summary: In cooperative Multi-Agent Reinforcement Learning (MARL) agents are required to learn behaviours as a team to achieve a common goal. While learning a task, some agents may end up learning sub-optimal policies, not contributing to the objective of the team. Such agents are called lazy agents due to their non-cooperative behaviours that may arise from failing to understand whether they caused the rewards.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In cooperative Multi-Agent Reinforcement Learning (MARL) agents are required to learn behaviours as a team to achieve a common goal. However, while learning a task, some agents may end up learning sub-optimal policies, not contributing to the objective of the team. Such agents are called lazy agents due to their non-cooperative behaviours that may arise from failing to understand whether they caused the rewards. As a consequence, we observe that the emergence of cooperative behaviours is not necessarily a byproduct of being able to solve a task as a team. In this paper, we investigate the applications of causality in MARL and how it can be applied in MARL to penalise these lazy agents. We observe that causality estimations can be used to improve the credit assignment to the agents and show how it can be leveraged to improve independent learning in MARL. Furthermore, we investigate how Amortized Causal Discovery can be used to automate causality detection within MARL environments. The results demonstrate that causality relations between individual observations and the team reward can be used to detect and punish lazy agents, making them develop more intelligent behaviours. This results in improvements not only in the overall performances of the team but also in their individual capabilities. In addition, results show that Amortized Causal Discovery can be used efficiently to find causal relations in MARL.

Related papers

• Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games [87.5673042805229]
  How large language models balance self-interest and collective well-being is a critical challenge for ensuring alignment, robustness, and safe deployment.<n>We adapt a public goods game with institutional choice from behavioral economics, allowing us to observe how different LLMs navigate social dilemmas.<n>Surprisingly, we find that reasoning LLMs, such as the o1 series, struggle significantly with cooperation.
  arXiv  Detail & Related papers  (2025-06-29T15:02:47Z)
• How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior [49.62361184944454]
  Memory is a critical component in large language model (LLM)-based agents.<n>We study how memory management choices impact the LLM agents' behavior, especially their long-term performance.
  arXiv  Detail & Related papers  (2025-05-21T22:35:01Z)
• A Roadmap Towards Improving Multi-Agent Reinforcement Learning With Causal Discovery And Inference [0.24578723416255746]
  Causal reasoning is increasingly used in Reinforcement Learning (RL) to improve the learning process. However, applications of causal reasoning to Multi-Agent RL (MARL) are still mostly unexplored. We take the first step in investigating the opportunities and challenges of applying causal reasoning in MARL.
  arXiv  Detail & Related papers  (2025-03-22T15:49:13Z)
• Variable-Agnostic Causal Exploration for Reinforcement Learning [56.52768265734155]
  We introduce a novel framework, Variable-Agnostic Causal Exploration for Reinforcement Learning (VACERL) Our approach automatically identifies crucial observation-action steps associated with key variables using attention mechanisms. It constructs the causal graph connecting these steps, which guides the agent towards observation-action pairs with greater causal influence on task completion.
  arXiv  Detail & Related papers  (2024-07-17T09:45:27Z)
• Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning [18.054709749075194]
  We propose a novel MARL algorithm named Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning (SCIC) Our approach aims to detect inter-agent causal influences in specific situations based on the criterion using causal intervention and conditional mutual information. The resulting update links coordinated exploration and intrinsic reward distribution, which enhance overall collaboration and performance.
  arXiv  Detail & Related papers  (2023-12-15T05:09:32Z)
• On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL [5.124364759305485]
  We argue that relying solely on the empirical returns may obscure crucial insights into agent behaviour. In this paper, we explore the application of explainable AI (XAI) tools to gain profound insights into agent behaviour.
  arXiv  Detail & Related papers  (2023-12-13T19:10:10Z)
• Learning Independently from Causality in Multi-Agent Environments [0.0]
  Multi-Agent Reinforcement Learning (MARL) comprises an area of growing interest in the field of machine learning. The lazy agent pathology is a famous problem in MARL that denotes the event when some of the agents in a MARL team do not contribute to the common goal. We study a fully decentralised MARL setup where agents need to learn cooperation strategies and show that there is a causal relation between individual observations and the team reward.
  arXiv  Detail & Related papers  (2023-11-05T19:12:08Z)
• Causality Detection for Efficient Multi-Agent Reinforcement Learning [0.0]
  We show how causality can be used to penalise lazy agents and improve their behaviours. We show empirically that using causality estimations in Multi-Agent Reinforcement Learning improves not only the holistic performance of the team, but also the individual capabilities of each agent.
  arXiv  Detail & Related papers  (2023-03-24T18:47:44Z)
• Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning [85.86440477005523]
  We study more human-like RL agents which incorporate an established model of human-irrationality, the Rational Inattention (RI) model. RIRL models the cost of cognitive information processing using mutual information. We show that using RIRL yields a rich spectrum of new equilibrium behaviors that differ from those found under rational assumptions.
  arXiv  Detail & Related papers  (2022-01-18T20:54:00Z)
• Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration [40.87053312548429]
  We introduce a novel Episodic Multi-agent reinforcement learning with Curiosity-driven exploration, called EMC. We use prediction errors of individual Q-values as intrinsic rewards for coordinated exploration and utilize episodic memory to exploit explored informative experience to boost policy training.
  arXiv  Detail & Related papers  (2021-11-22T07:34:47Z)
• On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning [55.95253619768565]
  Current MARL algorithms assume that the number of agents within a group remains fixed throughout an experiment. In many practical problems, an agent may terminate before their teammates. We present a novel architecture for an existing state-of-the-art MARL algorithm which uses attention instead of a fully connected layer with absorbing states.
  arXiv  Detail & Related papers  (2021-11-10T23:45:08Z)
• What is Going on Inside Recurrent Meta Reinforcement Learning Agents? [63.58053355357644]
  Recurrent meta reinforcement learning (meta-RL) agents are agents that employ a recurrent neural network (RNN) for the purpose of "learning a learning algorithm" We shed light on the internal working mechanisms of these agents by reformulating the meta-RL problem using the Partially Observable Markov Decision Process (POMDP) framework.
  arXiv  Detail & Related papers  (2021-04-29T20:34:39Z)
• UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning [53.73686229912562]
  We propose a novel MARL approach called Universal Value Exploration (UneVEn) UneVEn learns a set of related tasks simultaneously with a linear decomposition of universal successor features. Empirical results on a set of exploration games, challenging cooperative predator-prey tasks requiring significant coordination among agents, and StarCraft II micromanagement benchmarks show that UneVEn can solve tasks where other state-of-the-art MARL methods fail.
  arXiv  Detail & Related papers  (2020-10-06T19:08:47Z)
• Learning to Incentivize Other Learning Agents [73.03133692589532]
  We show how to equip RL agents with the ability to give rewards directly to other agents, using a learned incentive function. Such agents significantly outperform standard RL and opponent-shaping agents in challenging general-sum Markov games. Our work points toward more opportunities and challenges along the path to ensure the common good in a multi-agent future.
  arXiv  Detail & Related papers  (2020-06-10T20:12:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.