The Good Shepherd: An Oracle Agent for Mechanism Design

• URL: http://arxiv.org/abs/2202.10135v1
• Date: Mon, 21 Feb 2022 11:28:09 GMT
• Title: The Good Shepherd: An Oracle Agent for Mechanism Design
• Authors: Jan Balaguer, Raphael Koster, Christopher Summerfield, Andrea Tacchetti
• Abstract summary: We propose an algorithm for constructing agents that perform well when evaluated over the learning trajectory of their adaptive co-players. Our results show that our mechanisms are able to shepherd the participants strategies towards favorable outcomes.
• Score: 6.226991885861965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: From social networks to traffic routing, artificial learning agents are playing a central role in modern institutions. We must therefore understand how to leverage these systems to foster outcomes and behaviors that align with our own values and aspirations. While multiagent learning has received considerable attention in recent years, artificial agents have been primarily evaluated when interacting with fixed, non-learning co-players. While this evaluation scheme has merit, it fails to capture the dynamics faced by institutions that must deal with adaptive and continually learning constituents. Here we address this limitation, and construct agents ("mechanisms") that perform well when evaluated over the learning trajectory of their adaptive co-players ("participants"). The algorithm we propose consists of two nested learning loops: an inner loop where participants learn to best respond to fixed mechanisms; and an outer loop where the mechanism agent updates its policy based on experience. We report the performance of our mechanism agents when paired with both artificial learning agents and humans as co-players. Our results show that our mechanisms are able to shepherd the participants strategies towards favorable outcomes, indicating a path for modern institutions to effectively and automatically influence the strategies and behaviors of their constituents.

Related papers

• Don't lie to your friends: Learning what you know from collaborative self-play [90.35507959579331]
  We propose a radically new approach to teaching AI agents what they know. We construct multi-agent collaborations in which the group is rewarded for collectively arriving at correct answers. The desired meta-knowledge emerges from the incentives built into the structure of the interaction.
  arXiv  Detail & Related papers  (2025-03-18T17:53:20Z)
• Multi-agent cooperation through learning-aware policy gradients [53.63948041506278]
  Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning. We present the first unbiased, higher-derivative-free policy gradient algorithm for learning-aware reinforcement learning. We derive from the iterated prisoner's dilemma a novel explanation for how and when cooperation arises among self-interested learning-aware agents.
  arXiv  Detail & Related papers  (2024-10-24T10:48:42Z)
• Reciprocal Reward Influence Encourages Cooperation From Self-Interested Agents [2.1301560294088318]
  Cooperation between self-interested individuals is a widespread phenomenon in the natural world, but remains elusive in interactions between artificially intelligent agents. We introduce Reciprocators, reinforcement learning agents which are intrinsically motivated to reciprocate the influence of opponents' actions on their returns. We show that Reciprocators can be used to promote cooperation in temporally extended social dilemmas during simultaneous learning.
  arXiv  Detail & Related papers  (2024-06-03T06:07:27Z)
• ProAgent: Building Proactive Cooperative Agents with Large Language Models [89.53040828210945]
  ProAgent is a novel framework that harnesses large language models to create proactive agents. ProAgent can analyze the present state, and infer the intentions of teammates from observations. ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
  arXiv  Detail & Related papers  (2023-08-22T10:36:56Z)
• Homomorphism Autoencoder -- Learning Group Structured Representations from Observed Transitions [51.71245032890532]
  We propose methods enabling an agent acting upon the world to learn internal representations of sensory information consistent with actions that modify it. In contrast to existing work, our approach does not require prior knowledge of the group and does not restrict the set of actions the agent can perform.
  arXiv  Detail & Related papers  (2022-07-25T11:22:48Z)
• Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning [114.36124979578896]
  We design a dynamic mechanism using offline reinforcement learning algorithms. Our algorithm is based on the pessimism principle and only requires a mild assumption on the coverage of the offline data set.
  arXiv  Detail & Related papers  (2022-05-05T05:44:26Z)
• HCMD-zero: Learning Value Aligned Mechanisms from Data [11.146694178077565]
  HCMD-zero is a general purpose method to construct mechanism agents. It learns by mediating interactions among participants, while remaining engaged in an electoral contest with copies of itself. Our results show that HCMD-zero produces competitive mechanism agents that are consistently preferred by human participants.
  arXiv  Detail & Related papers  (2022-02-21T11:13:53Z)
• 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)
• Modelling Cooperation in Network Games with Spatio-Temporal Complexity [11.665246332943058]
  We study the emergence of self-organized cooperation in complex gridworld domains. Using multi-agent deep reinforcement learning, we simulate an agent society for a variety of plausible mechanisms. Our methods have implications for mechanism design in both human and artificial agent systems.
  arXiv  Detail & Related papers  (2021-02-13T12:04:52Z)
• Learning Latent Representations to Influence Multi-Agent Interaction [65.44092264843538]
  We propose a reinforcement learning-based framework for learning latent representations of an agent's policy. We show that our approach outperforms the alternatives and learns to influence the other agent.
  arXiv  Detail & Related papers  (2020-11-12T19:04:26Z)
• Parallel Knowledge Transfer in Multi-Agent Reinforcement Learning [0.2538209532048867]
  This paper proposes a novel knowledge transfer framework in MARL, PAT (Parallel Attentional Transfer) We design two acting modes in PAT, student mode and self-learning mode. When agents are unfamiliar with the environment, the shared attention mechanism in student mode effectively selects learning knowledge from other agents to decide agents' actions.
  arXiv  Detail & Related papers  (2020-03-29T17:42:00Z)
• On Simple Reactive Neural Networks for Behaviour-Based Reinforcement Learning [5.482532589225552]
  We present a behaviour-based reinforcement learning approach, inspired by Brook's subsumption architecture. Our working assumption is that a pick and place robotic task can be simplified by leveraging domain knowledge of a robotics developer. Our approach learns the pick and place task in 8,000 episodes, which represents a drastic reduction in the number of training episodes required by an end-to-end approach.
  arXiv  Detail & Related papers  (2020-01-22T11:49:52Z)

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.