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Zipeng Dai

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3

AAMAS Conference 2025 Conference Paper

Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction

  • Taher Jafferjee
  • Juliusz Ziomek
  • Tianpei Yang
  • Zipeng Dai
  • Jianhong Wang
  • Matthew E. Taylor
  • Kun Shao
  • Jun Wang

Multi-agent reinforcement learning (MARL) enables systems of autonomous agents to solve complex tasks from jointly gathered experiences of the environment. Many MARL algorithms perform centralized training (CT), often in a simulated environment, where at each time-step the critic makes use of a single sample of the agents’ joint-action for training. Yet, as agents update their policies during training, these single samples may poorly represent the agents’ joint-policy leading to high variance gradient estimates that hinder learning. In this paper, we examine the effect on MARL estimators of allowing the number of joint-action samples taken at each time-step to be greater than 1 in training. Our theoretical analysis shows that even modestly increasing the number of jointaction samples shown to the critic leads to TD updates that closely approximate the true expected value under the current joint-policy. In particular, we prove this reduces variance in value estimates similar to that of decentralized training while maintaining the learning benefits of CT. We describe how such a protocol can be seamlessly realized by sharing policy parameters between the agents during training and apply the technique to induce lower variance in estimates in MARL methods within a general apparatus which we call Performance Enhancing Reinforcement Learning Apparatus (PERLA). Lastly, we demonstrate PERLA’s performance improvements and estimator variance reduction capabilities in a range of environments including Multi-agent Mujoco, and StarCraft II. ∗Work was conducted while at Huawei R&D. †Corresponding author. This work is licensed under a Creative Commons Attribution International 4. 0 License. Proc. of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025), Y. Vorobeychik, S. Das, A. Nowé (eds.), May 19 – 23, 2025, Detroit, Michigan, USA. © 2025 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org).

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AAAI Conference 2023 Conference Paper

Learning to Shape Rewards Using a Game of Two Partners

  • David Mguni
  • Taher Jafferjee
  • Jianhong Wang
  • Nicolas Perez-Nieves
  • Wenbin Song
  • Feifei Tong
  • Matthew Taylor
  • Tianpei Yang

Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards. However, RS typically relies on manually engineered shaping-reward functions whose construc- tion is time-consuming and error-prone. It also requires domain knowledge which runs contrary to the goal of autonomous learning. We introduce Reinforcement Learning Optimising Shaping Algorithm (ROSA), an automated reward shaping framework in which the shaping-reward function is constructed in a Markov game between two agents. A reward-shaping agent (Shaper) uses switching controls to determine which states to add shaping rewards for more efficient learning while the other agent (Controller) learns the optimal policy for the task using these shaped rewards. We prove that ROSA, which adopts existing RL algorithms, learns to construct a shaping-reward function that is beneficial to the task thus ensuring efficient convergence to high performance policies. We demonstrate ROSA’s properties in three didactic experiments and show its superior performance against state-of-the-art RS algorithms in challenging sparse reward environments.

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ICLR Conference 2023 Conference Paper

Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints

  • David Henry Mguni
  • Aivar Sootla
  • Juliusz Krysztof Ziomek
  • Oliver Slumbers
  • Zipeng Dai
  • Kun Shao
  • Jun Wang 0012

Many real-world settings involve costs for performing actions; transaction costs in financial systems and fuel costs being common examples. In these settings, performing actions at each time step quickly accumulates costs leading to vastly suboptimal outcomes. Additionally, repeatedly acting produces wear and tear and ultimately, damage. Determining when to act is crucial for achieving successful outcomes and yet, the challenge of efficiently learning to behave optimally when actions incur minimally bounded costs remains unresolved. In this paper, we intro- duce a reinforcement learning (RL) framework named Learnable Impulse Control Reinforcement Algorithm (LICRA), for learning to optimally select both when to act and which actions to take when actions incur costs. At the core of LICRA is a nested structure that combines RL and a form of policy known as impulse control which learns to maximise objectives when actions incur costs. We prove that LICRA, which seamlessly adopts any RL method, converges to policies that optimally select when to perform actions and their optimal magnitudes. We then augment LICRA to handle problems in which the agent can perform at most k < ∞ actions and more generally, faces a budget constraint. We show LICRA learns the optimal value function and ensures budget constraints are satisfied almost surely. We demonstrate empirically LICRA’s superior performance against benchmark RL methods in OpenAI gym’s Lunar Lander and in Highway environments and a variant of the Merton portfolio problem within finance.

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