Interactive Inverse Reinforcement Learning for Cooperative Games

• URL: http://arxiv.org/abs/2111.04698v1
• Date: Mon, 8 Nov 2021 18:24:52 GMT
• Title: Interactive Inverse Reinforcement Learning for Cooperative Games
• Authors: Thomas Kleine Buening, Anne-Marie George, Christos Dimitrakakis
• Abstract summary: We study the problem of designing AI agents that can learn to cooperate effectively with a potentially suboptimal partner. This problem is modeled as a cooperative episodic two-agent Markov decision process. We show that when the learning agent's policies have a significant effect on the transition function, the reward function can be learned efficiently.
• Score: 7.257751371276486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the problem of designing AI agents that can learn to cooperate effectively with a potentially suboptimal partner while having no access to the joint reward function. This problem is modeled as a cooperative episodic two-agent Markov decision process. We assume control over only the first of the two agents in a Stackelberg formulation of the game, where the second agent is acting so as to maximise expected utility given the first agent's policy. How should the first agent act in order to learn the joint reward function as quickly as possible, and so that the joint policy is as close to optimal as possible? In this paper, we analyse how knowledge about the reward function can be gained in this interactive two-agent scenario. We show that when the learning agent's policies have a significant effect on the transition function, the reward function can be learned efficiently.

Related papers

• Principal-Agent Reward Shaping in MDPs [50.914110302917756]
  Principal-agent problems arise when one party acts on behalf of another, leading to conflicts of interest. We study a two-player Stack game where the principal and the agent have different reward functions, and the agent chooses an MDP policy for both players. Our results establish trees and deterministic decision processes with a finite horizon.
  arXiv  Detail & Related papers  (2023-12-30T18:30:44Z)
• 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)
• Decentralized scheduling through an adaptive, trading-based multi-agent system [1.7403133838762448]
  In multi-agent reinforcement learning systems, the actions of one agent can have a negative impact on the rewards of other agents. This work applies a trading approach to a simulated scheduling environment, where the agents are responsible for the assignment of incoming jobs to compute cores. The agents can trade the usage right of computational cores to process high-priority, high-reward jobs faster than low-priority, low-reward jobs.
  arXiv  Detail & Related papers  (2022-07-05T13:50:18Z)
• Cooperative Artificial Intelligence [0.0]
  We argue that there is a need for research on the intersection between game theory and artificial intelligence. We discuss the problem of how an external agent can promote cooperation between artificial learners. We show that the resulting cooperative outcome is stable in certain games even if the planning agent is turned off.
  arXiv  Detail & Related papers  (2022-02-20T16:50:37Z)
• Learning to Cooperate with Unseen Agent via Meta-Reinforcement Learning [4.060731229044571]
  Ad hoc teamwork problem describes situations where an agent has to cooperate with previously unseen agents to achieve a common goal. One could implement cooperative skills into an agent by using domain knowledge to design the agent's behavior. We apply meta-reinforcement learning (meta-RL) formulation in the context of the ad hoc teamwork problem.
  arXiv  Detail & Related papers  (2021-11-05T12:01:28Z)
• Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning [12.676356746752893]
  Training a multi-agent reinforcement learning (MARL) algorithm is more challenging than training a single-agent reinforcement learning algorithm. We propose an algorithm that boosts MARL training using the biased action information of other agents based on a friend-or-foe concept. We empirically demonstrate that our algorithm outperforms existing algorithms in various mixed cooperative-competitive environments.
  arXiv  Detail & Related papers  (2021-01-18T05:52:22Z)
• Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation [55.96893934962757]
  In multi-agent system, polices of different agents need to be evaluated jointly. In current methods, value functions or advantage functions use counter-factual joint actions which are evaluated asynchronously. In this work, we propose the approximatively synchronous advantage estimation.
  arXiv  Detail & Related papers  (2020-12-07T07:29:19Z)
• 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 Individually Inferred Communication for Multi-Agent Cooperation [37.56115000150748]
  We propose Individually Inferred Communication (I2C) to enable agents to learn a prior for agent-agent communication. The prior knowledge is learned via causal inference and realized by a feed-forward neural network. I2C can not only reduce communication overhead but also improve the performance in a variety of multi-agent cooperative scenarios.
  arXiv  Detail & Related papers  (2020-06-11T14:07:57Z)
• 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)
• Intrinsic Motivation for Encouraging Synergistic Behavior [55.10275467562764]
  We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks. Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own.
  arXiv  Detail & Related papers  (2020-02-12T19:34:51Z)

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.