Reducing Optimism Bias in Incomplete Cooperative Games

• URL: http://arxiv.org/abs/2402.01930v3
• Date: Sun, 15 Sep 2024 00:29:53 GMT
• Title: Reducing Optimism Bias in Incomplete Cooperative Games
• Authors: Filip Úradník, David Sychrovský, Jakub Černý, Martin Černý,
• Abstract summary: We present a framework aimed at optimizing the sequence for revealing coalition values in cooperative games. Our contributions are threefold: (i) we study the individual players' optimistic completions of games with missing coalition values along with the arising gap, and investigate its analytical characteristics that facilitate more efficient optimization; (ii) we develop methods to minimize this gap over classes of games with a known prior by disclosing values of additional coalitions in both offline and online fashion; and (iii) we empirically demonstrate the algorithms' performance in practical scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cooperative game theory has diverse applications in contemporary artificial intelligence, including domains like interpretable machine learning, resource allocation, and collaborative decision-making. However, specifying a cooperative game entails assigning values to exponentially many coalitions, and obtaining even a single value can be resource-intensive in practice. Yet simply leaving certain coalition values undisclosed introduces ambiguity regarding individual contributions to the collective grand coalition. This ambiguity often leads to players holding overly optimistic expectations, stemming from either inherent biases or strategic considerations, frequently resulting in collective claims exceeding the actual grand coalition value. In this paper, we present a framework aimed at optimizing the sequence for revealing coalition values, with the overarching goal of efficiently closing the gap between players' expectations and achievable outcomes in cooperative games. Our contributions are threefold: (i) we study the individual players' optimistic completions of games with missing coalition values along with the arising gap, and investigate its analytical characteristics that facilitate more efficient optimization; (ii) we develop methods to minimize this gap over classes of games with a known prior by disclosing values of additional coalitions in both offline and online fashion; and (iii) we empirically demonstrate the algorithms' performance in practical scenarios, together with an investigation into the typical order of revealing coalition values.

Related papers

• Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games [47.8980880888222]
  Multi-agent scenarios often involve mixed motives, demanding altruistic agents capable of self-protection against potential exploitation. We propose LASE Learning to balance Altruism and Self-interest based on Empathy. LASE allocates a portion of its rewards to co-players as gifts, with this allocation adapting dynamically based on the social relationship.
  arXiv  Detail & Related papers  (2024-10-10T12:30:56Z)
• Aligning Individual and Collective Objectives in Multi-Agent Cooperation [18.082268221987956]
  Mixed-motive cooperation is one of the most prominent challenges in multi-agent learning. We introduce a novel optimization method named textbftextitAltruistic textbftextitGradient textbftextitAdjustment (textbftextitAgA) that employs gradient adjustments to progressively align individual and collective objectives. We evaluate the effectiveness of our algorithm AgA through benchmark environments for testing mixed-motive collaboration with small-scale agents.
  arXiv  Detail & Related papers  (2024-02-19T08:18:53Z)
• Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination [36.33334853998621]
  We introduce the Cooperative Open-ended LEarning (COLE) framework to solve cooperative incompatibility in learning. COLE formulates open-ended objectives in cooperative games with two players using perspectives of graph theory to evaluate and pinpoint the cooperative capacity of each strategy. We show that COLE could effectively overcome the cooperative incompatibility from theoretical and empirical analysis.
  arXiv  Detail & Related papers  (2023-06-05T16:51:38Z)
• Cooperative Open-ended Learning Framework for Zero-shot Coordination [35.330951448600594]
  We propose a framework to construct open-ended objectives in cooperative games with two players. We also propose a practical algorithm that leverages knowledge from game theory and graph theory. Our method outperforms current state-of-the-art methods when coordinating with different-level partners.
  arXiv  Detail & Related papers  (2023-02-09T18:37:04Z)
• Offline Learning in Markov Games with General Function Approximation [22.2472618685325]
  We study offline multi-agent reinforcement learning (RL) in Markov games. We provide the first framework for sample-efficient offline learning in Markov games.
  arXiv  Detail & Related papers  (2023-02-06T05:22:27Z)
• Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions [145.54544979467872]
  We propose and analyze new fictitious play policy optimization algorithms for zero-sum Markov games with structured but unknown transitions. We prove tight $widetildemathcalO(sqrtK)$ regret bounds after $K$ episodes in a two-agent competitive game scenario. Our algorithms feature a combination of Upper Confidence Bound (UCB)-type optimism and fictitious play under the scope of simultaneous policy optimization.
  arXiv  Detail & Related papers  (2022-07-25T18:29:16Z)
• A unified stochastic approximation framework for learning in games [82.74514886461257]
  We develop a flexible approximation framework for analyzing the long-run behavior of learning in games (both continuous and finite) The proposed analysis template incorporates a wide array of popular learning algorithms, including gradient-based methods, exponential/multiplicative weights for learning in finite games, optimistic and bandit variants of the above, etc.
  arXiv  Detail & Related papers  (2022-06-08T14:30:38Z)
• Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation [62.96628432641806]
  Scene Graph Generation aims to first encode the visual contents within the given image and then parse them into a compact summary graph. We first present a novel Stacked Hybrid-Attention network, which facilitates the intra-modal refinement as well as the inter-modal interaction. We then devise an innovative Group Collaborative Learning strategy to optimize the decoder.
  arXiv  Detail & Related papers  (2022-03-18T09:14:13Z)
• Provably Efficient Algorithms for Multi-Objective Competitive RL [54.22598924633369]
  We study multi-objective reinforcement learning (RL) where an agent's reward is represented as a vector. In settings where an agent competes against opponents, its performance is measured by the distance of its average return vector to a target set. We develop statistically and computationally efficient algorithms to approach the associated target set.
  arXiv  Detail & Related papers  (2021-02-05T14:26:00Z)
• Faster Algorithms for Optimal Ex-Ante Coordinated Collusive Strategies in Extensive-Form Zero-Sum Games [123.76716667704625]
  We focus on the problem of finding an optimal strategy for a team of two players that faces an opponent in an imperfect-information zero-sum extensive-form game. In that setting, it is known that the best the team can do is sample a profile of potentially randomized strategies (one per player) from a joint (a.k.a. correlated) probability distribution at the beginning of the game. We provide an algorithm that computes such an optimal distribution by only using profiles where only one of the team members gets to randomize in each profile.
  arXiv  Detail & Related papers  (2020-09-21T17:51:57Z)
• Finding Core Members of Cooperative Games using Agent-Based Modeling [0.0]
  Agent-based modeling (ABM) is a powerful paradigm to gain insight into social phenomena. In this paper, a algorithm is developed that can be embedded into an ABM to allow the agents to find coalition.
  arXiv  Detail & Related papers  (2020-08-30T17:38:43Z)

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.