mlsanityData to 14 Oct 2026

Game Redesign in No-regret Game Playing

arXiv 2110.11763 ↗First posted 18 Oct 2021cs.GT cs.LGcited by 8

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Abstract (arXiv)

We study the game redesign problem in which an external designer has the ability to change the payoff function in each round, but incurs a design cost for deviating from the original game. The players apply no-regret learning algorithms to repeatedly play the changed games with limited feedback. The goals of the designer are to (i) incentivize all players to take a specific target action profile frequently; and (ii) incur small cumulative design cost. We present game redesign algorithms with the guarantee that the target action profile is played in T-o(T) rounds while incurring only o(T) cumulative design cost. Game redesign describes both positive and negative applications: a benevolent designer who incentivizes players to take a target action profile with better social welfare compared to the solution of the original game, or a malicious attacker whose target action profile benefits themselves but not the players. Simulations on four classic games confirm the effectiveness of our proposed redesign algorithms.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 13 of its 31 references that are in the map. First posted on arXiv 18 Oct 2021.

Builds on

13 of 31 references.

References · 13

  1. 2019Introduction to Multi-Armed Banditscited by 481
  2. 2020Adaptive Reward-Poisoning Attacks against Reinforcement Learningcited by 86
  3. 2020Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learningcited by 81
  4. 2019Policy Poisoning in Batch Reinforcement Learning and Controlcited by 70
  5. 2020Stochastic Linear Bandits Robust to Adversarial Attackscited by 65
  6. 2018Adversarial Attacks on Stochastic Banditscited by 57
  7. 2019Data Poisoning Attacks on Stochastic Banditscited by 53
  8. 2019Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signalscited by 50
  9. 2020Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamicscited by 38
  10. 2020Adversarial Attacks on Linear Contextual Banditscited by 34
  11. 2021Robust Policy Gradient against Strong Data Corruptioncited by 30
  12. 2018Data Poisoning Attacks in Contextual Banditscited by 23
1 more
  1. 2020Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attackcited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 1; 2025: 1; 2026: 1.

8 papers.

  1. 2022Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learningcited by 20
  2. 2023Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learningcited by 10
  3. 2023Adversarial Attacks on Adversarial Banditscited by 6
  4. 2024Corruption-Robust Offline Two-Player Zero-Sum Markov Gamescited by 4
  5. 2023Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Valuecited by 3
  6. 2023Data Poisoning to Fake a Nash Equilibrium in Markov Gamescited by 1
  7. 2025Optimally Installing Strict Equilibriacited by 0
  8. 2026When Can You Poison Rewards? A Tight Characterization of Reward Poisoning in Linear MDPscited by 0