We would like to thank all reviewers for reading our paper and providing constructive comments. Sometimes, the primary interest is to understand agent behaviors, and hence only the learning mode is needed. Alternatively, when all game inputs are known, the focus is on the intervention mode. In the final version, we will (i) explain in 2.1 how these We agree that it is neither rigorous nor necessary to assert that "most" Our work is inspired by the current interests on complex optimization-based layers. It is the first to treat VIs as individual layers in the end-to-end framework.

We perform extensive experiments across several benchmark environments and RL methods.

Hilbert space (RKHS), i.e., the expected reward is linear w.r.t. an action feature map of possibly

We study multi-agent reinforcement learning (MARL) in infinite-horizon discounted zero-sum Markov games.

V100 GPUs to train the models. Consortium and are licensed under a Creative Commons Attribution 4.0 License. Similarly, for evaluating the agent listener with a human speaker, each agent evaluates 400 human utterances in Fig 5b. In Fig 10, we present the results of the human evaluation on the text game. Sec 4.3, we show that agents trained using our method beat all prior baselines when paired with both The blue bars show the standard deviation across all agents present in the buffer.

Reinforcement learning (RL) has emerged as a pivotal technique for fine-tuning large language models (LLMs) on specific tasks. However, prevailing RL fine-tuning methods predominantly rely on PPO and its variants. Though these algorithms are effective in general RL settings, they often exhibit suboptimal performance and vulnerability to distribution collapse when applied to the fine-tuning of LLMs.

To help address these issues, a natural approach is to consider games with special structures. In this paper, we focus on congestion games.