Reputation-driven Decision-making in Networks of Stochastic Agents
- URL: http://arxiv.org/abs/2008.11791v2
- Date: Tue, 20 Oct 2020 07:57:32 GMT
- Title: Reputation-driven Decision-making in Networks of Stochastic Agents
- Authors: David Maoujoud and Gavin Rens
- Abstract summary: We propose a Markov Decision Process-derived framework, called RepNet-MDP, tailored to domains in which agent reputation is a key driver of the interactions between agents.
In a series of experiments, RepNet agents are shown to be able to adapt their own behavior to the past behavior and reliability of the remaining agents of the network.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper studies multi-agent systems that involve networks of
self-interested agents. We propose a Markov Decision Process-derived framework,
called RepNet-MDP, tailored to domains in which agent reputation is a key
driver of the interactions between agents. The fundamentals are based on the
principles of RepNet-POMDP, a framework developed by Rens et al. in 2018, but
addresses its mathematical inconsistencies and alleviates its intractability by
only considering fully observable environments. We furthermore use an online
learning algorithm for finding approximate solutions to RepNet-MDPs. In a
series of experiments, RepNet agents are shown to be able to adapt their own
behavior to the past behavior and reliability of the remaining agents of the
network. Finally, our work identifies a limitation of the framework in its
current formulation that prevents its agents from learning in circumstances in
which they are not a primary actor.
Related papers
- Agent-as-a-Judge: Evaluate Agents with Agents [61.33974108405561]
We introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems.
This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving process.
We present DevAI, a new benchmark of 55 realistic automated AI development tasks.
arXiv Detail & Related papers (2024-10-14T17:57:02Z) - R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models [50.19174067263255]
We introduce prior preference learning techniques and self-revision schedules to help the agent excel in sparse-reward, continuous action, goal-based robotic control POMDP environments.
We show that our agents offer improved performance over state-of-the-art models in terms of cumulative rewards, relative stability, and success rate.
arXiv Detail & Related papers (2024-09-21T18:32:44Z) - Interactive Autonomous Navigation with Internal State Inference and
Interactivity Estimation [58.21683603243387]
We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework.
These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents.
Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
arXiv Detail & Related papers (2023-11-27T18:57:42Z) - Continuous-Time Distributed Dynamic Programming for Networked Multi-Agent Markov Decision Processes [7.464789724562025]
This paper investigates continuous-time distributed dynamic programming (DP) algorithms for networked multi-agent Markov decision problems (MAMDPs)
In our study, we adopt a distributed multi-agent framework where individual agents have access only to their own rewards, lacking insights into the rewards of other agents.
arXiv Detail & Related papers (2023-07-31T14:25:20Z) - Policy Evaluation in Decentralized POMDPs with Belief Sharing [39.550233049869036]
We consider a cooperative policy evaluation task in which agents are not assumed to observe the environment state directly.
We propose a fully decentralized belief forming strategy that relies on individual updates and on localized interactions over a communication network.
arXiv Detail & Related papers (2023-02-08T15:54:15Z) - Reward-Sharing Relational Networks in Multi-Agent Reinforcement Learning
as a Framework for Emergent Behavior [0.0]
We integrate social' interactions into the MARL setup through a user-defined relational network.
We examine the effects of agent-agent relations on the rise of emergent behaviors.
arXiv Detail & Related papers (2022-07-12T23:27:42Z) - Emergence of Theory of Mind Collaboration in Multiagent Systems [65.97255691640561]
We propose an adaptive training algorithm to develop effective collaboration between agents with ToM.
We evaluate our algorithms with two games, where our algorithm surpasses all previous decentralized execution algorithms without modeling ToM.
arXiv Detail & Related papers (2021-09-30T23:28:00Z) - 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) - Multi-Agent Decentralized Belief Propagation on Graphs [0.0]
We consider the problem of interactive partially observable Markov decision processes (I-POMDPs)
We propose a decentralized belief propagation algorithm for the problem.
Our work appears to be the first study of decentralized belief propagation algorithm for networked multi-agent I-POMDPs.
arXiv Detail & Related papers (2020-11-06T18:16:26Z) - Maximizing Information Gain in Partially Observable Environments via
Prediction Reward [64.24528565312463]
This paper tackles the challenge of using belief-based rewards for a deep RL agent.
We derive the exact error between negative entropy and the expected prediction reward.
This insight provides theoretical motivation for several fields using prediction rewards.
arXiv Detail & Related papers (2020-05-11T08:13:49Z)
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.