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Found 13 papers, 3 papers with code
Differentially Private Reinforcement Learning with Self-Play
We study the problem of multi-agent reinforcement learning (multi-agent RL) with differential privacy (DP) constraints.
Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints
We study the problem of multi-agent reinforcement learning (MARL) with adaptivity constraints -- a new problem motivated by real-world applications where deployments of new policies are costly and the number of policy updates must be minimized.
OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques.
GameEval: Evaluating LLMs on Conversational Games
In this paper, we propose GameEval, a novel approach to evaluating LLMs through goal-driven conversational games, overcoming the limitations of previous methods.
Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data
Developing theoretical guarantees on the sample complexity of offline RL methods is an important step towards making data-hungry RL algorithms practically viable.
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning
The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved.
Logarithmic Switching Cost in Reinforcement Learning beyond Linear MDPs
In many real-life reinforcement learning (RL) problems, deploying new policies is costly.
Near-Optimal Differentially Private Reinforcement Learning
We close this gap for the JDP case by designing an $\epsilon$-JDP algorithm with a regret of $\widetilde{O}(\sqrt{SAH^2T}+S^2AH^3/\epsilon)$ which matches the information-theoretic lower bound of non-private learning for all choices of $\epsilon> S^{1. 5}A^{0. 5} H^2/\sqrt{T}$.
SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup Training
The conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downstream tasks.
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation
We study the problem of deployment efficient reinforcement learning (RL) with linear function approximation under the \emph{reward-free} exploration setting.
Doubly Fair Dynamic Pricing
We show that a doubly fair policy must be random to have higher revenue than the best trivial policy that assigns the same price to different groups.
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost
In this paper, we propose a new algorithm based on stage-wise exploration and adaptive policy elimination that achieves a regret of $\widetilde{O}(\sqrt{H^4S^2AT})$ while requiring a switching cost of $O(HSA \log\log T)$.
Novel Nussbaum-Type Function based Safe Adaptive Distributed Consensus Control with Arbitrary Unknown Control Direction
This paper develops a novel saturated Nussbaum function to relax such limitations and proposes a Nussbaum function based control scheme for the consensus problem of multi-agent systems with arbitrary non-identical unknown control directions and safe control progress.
