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Found 13 papers, 4 papers with code
Yi-Lightning Technical Report
This technical report presents Yi-Lightning, our latest flagship large language model (LLM).
Zero-Indexing Internet Search Augmented Generation for Large Language Models
We conduct extensive empirical studies to evaluate the performance of this Internet search augmented generation paradigm.
Yi: Open Foundation Models by 01.AI
The Yi model family is based on 6B and 34B pretrained language models, then we extend them to chat models, 200K long context models, depth-upscaled models, and vision-language models.
Ranked #1 on
Chatbot
on AlpacaEval
(using extra training data)
JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games
This paper presents an empirical exploration of non-transitivity in perfect-information games, specifically focusing on Xiangqi, a traditional Chinese board game comparable in game-tree complexity to chess and shogi.
An Empirical Study on Google Research Football Multi-agent Scenarios
Few multi-agent reinforcement learning (MARL) research on Google Research Football (GRF) focus on the 11v11 multi-agent full-game scenario and to the best of our knowledge, no open benchmark on this scenario has been released to the public.
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection
The exploration problem is one of the main challenges in deep reinforcement learning (RL).
Ranked #1 on
Atari Games
on Atari-57
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning
In this way, agents dealing with the same subtask share their learning of specific abilities and different subtasks correspond to different specific abilities.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning
In this way, CTDS balances the full utilization of global observation during training and the feasibility of decentralized execution for online inference.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning
Specifically, we model both individual Q-values and global Q-value with categorical distribution.
Revisiting QMIX: Discriminative Credit Assignment by Gradient Entropy Regularization
In cooperative multi-agent systems, agents jointly take actions and receive a team reward instead of individual rewards.
Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Intervention
Safety has become one of the main challenges of applying deep reinforcement learning to real world systems.
An Optimal Resource Allocator of Elastic Training for Deep Learning Jobs on Cloud
Cloud training platforms, such as Amazon Web Services and Huawei Cloud provide users with computational resources to train their deep learning jobs.
Learning to Shape Rewards using a Game of Two Partners
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards.
