Search Results for author:
Found 27 papers, 6 papers with code
X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner
The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights.
PET-SQL: A Prompt-enhanced Two-stage Text-to-SQL Framework with Cross-consistency
Then, in the first stage, question-SQL pairs are retrieved as few-shot demonstrations, prompting the LLM to generate a preliminary SQL (PreSQL).
Ranked #1 on
Text-To-SQL
on spider
Benchmarking the Text-to-SQL Capability of Large Language Models: A Comprehensive Evaluation
Then we formulate five evaluation tasks to comprehensively assess the performance of diverse methods across various LLMs throughout the Text-to-SQL process. Our study highlights the performance disparities among LLMs and proposes optimal in-context learning solutions tailored to each task.
PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning
Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL.
DuaLight: Enhancing Traffic Signal Control by Leveraging Scenario-Specific and Scenario-Shared Knowledge
Furthermore, we implement a scenario-shared Co-Train module to facilitate the learning of generalizable dynamics information across different scenarios.
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach
The remarkable progress in Large Language Models (LLMs) opens up new avenues for addressing planning and decision-making problems in Multi-Agent Systems (MAS).
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems
Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend of task planning and the utilization of external tools, such as APIs.
Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain
Our experiments with Large Language Models (LLMs) illustrate the significant performance improvement on the business dataset and prove the substantial potential of our method.
TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage
With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications.
Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems
Our research contributes to the development of an effective and adaptable asynchronous action coordination method that can be widely applied to various task types and environmental configurations in MAS.
Transformer in Transformer as Backbone for Deep Reinforcement Learning
Recent methods combine the Transformer with these modules for better performance.
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning
Furthermore, we introduce a novel paradigm named Personalized Training with Distilled Execution (PTDE), wherein agent-personalized global information is distilled into the agent's local information.
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework
To break this curse, we propose a unified agent permutation framework that exploits the permutation invariance (PI) and permutation equivariance (PE) inductive biases to reduce the multiagent state space.
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition
The MineRL competition is designed for the development of reinforcement learning and imitation learning algorithms that can efficiently leverage human demonstrations to drastically reduce the number of environment interactions needed to solve the complex \emph{ObtainDiamond} task with sparse rewards.
Learning Explicit Credit Assignment for Multi-agent Joint Q-learning
In contrast, we formulate an \emph{explicit} credit assignment problem where each agent gives its suggestion about how to weight individual Q-values to explicitly maximize the joint Q-value, besides guaranteeing the Bellman optimality of the joint Q-value.
Towards robust and domain agnostic reinforcement learning competitions
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field.
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
In addition, we use a novel agent network named Population Invariant agent with Transformer (PIT) to realize the coordination transfer in more varieties of scenarios.
Learning State Representations via Temporal Cycle-Consistency Constraint in Model-Based Reinforcement Learning
Representation learning is a popular approach for reinforcement learning (RL) tasks with partially observable Markov decision processes.
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only the state (and action) but also an explicit policy representation.
Transfer among Agents: An Efficient Multiagent Transfer Learning Framework
In many cases, each agent's experience is inconsistent with each other which causes the option-value estimation to oscillate and to become inaccurate.
Open-Ended Question Answering
Reinforcement Learning (RL)
+1
Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing
In this paper, we study reward design problem in cooperative MARL based on packet routing environments.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning Agent Communication under Limited Bandwidth by Message Pruning
We evaluate the gating mechanism on several tasks.
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning
Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about their environments, they are more likely to achieve better cooperation.
Learning Multi-agent Communication under Limited-bandwidth Restriction for Internet Packet Routing
Communication is an important factor for the big multi-agent world to stay organized and productive.
Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG
Second, to model the teammates' policies using the collected information in an effective way, ATT-MADDPG enhances the centralized critic with an attention mechanism.
ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning
Communication is a critical factor for the big multi-agent world to stay organized and productive.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Predicting Restaurant Consumption Level through Social Media Footprints
Accurate prediction of user attributes from social media is valuable for both social science analysis and consumer targeting.
