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Found 22 papers, 9 papers with code
Token-level Direct Preference Optimization
Fine-tuning pre-trained Large Language Models (LLMs) is essential to align them with human values and intentions.
Learning Macroeconomic Policies based on Microfoundations: A Stackelberg Mean Field Game Approach
Effective macroeconomic policies play a crucial role in promoting economic growth and social stability.
Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach
StarCraft II is a challenging benchmark for AI agents due to the necessity of both precise micro level operations and strategic macro awareness.
AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System
This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation.
Ask more, know better: Reinforce-Learned Prompt Questions for Decision Making with Large Language Models
To that purpose, we offer a new leader-follower bilevel framework that is capable of learning to ask relevant questions (prompts) and subsequently undertaking reasoning to guide the learning of actions.
Large Sequence Models for Sequential Decision-Making: A Survey
Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e. g., GPT-3 and Swin Transformer.
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.
Contextual Transformer for Offline Meta Reinforcement Learning
Firstly, we propose prompt tuning for offline RL, where a context vector sequence is concatenated with the input to guide the conditional policy generation.
Learning to Identify Top Elo Ratings: A Dueling Bandits Approach
The Elo rating system is widely adopted to evaluate the skills of (chess) game and sports players.
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning
Gradient-based Meta-RL (GMRL) refers to methods that maintain two-level optimisation procedures wherein the outer-loop meta-learner guides the inner-loop gradient-based reinforcement learner to achieve fast adaptations.
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks
In this paper, we facilitate the research by providing large-scale datasets, and use them to examine the usage of the Decision Transformer in the context of MARL.
A Game-Theoretic Approach for Improving Generalization Ability of TSP Solvers
In this paper, we introduce a two-player zero-sum framework between a trainable \emph{Solver} and a \emph{Data Generator} to improve the generalization ability of deep learning-based solvers for Traveling Salesman Problem (TSP).
Offline Pre-trained Multi-Agent Decision Transformer
Offline reinforcement learning leverages static datasets to learn optimal policies with no necessity to access the environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Settling the Variance of Multi-Agent Policy Gradients
In multi-agent RL (MARL), although the PG theorem can be naturally extended, the effectiveness of multi-agent PG (MAPG) methods degrades as the variance of gradient estimates increases rapidly with the number of agents.
Learning Predictive Communication by Imagination in Networked System Control
Dealing with multi-agent control in networked systems is one of the biggest challenges in Reinforcement Learning (RL) and limited success has been presented compared to recent deep reinforcement learning in single-agent domain.
Improving Knowledge Tracing via Pre-training Question Embeddings
Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response.
Ranked #3 on
Knowledge Tracing
on EdNet
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning
Although existing works formulate this problem into a centralized learning with decentralized execution framework, which avoids the non-stationary problem in training, their decentralized execution paradigm limits the agents' capability to coordinate.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Bi-level Actor-Critic for Multi-agent Coordination
Coordination is one of the essential problems in multi-agent systems.
Multiagent Systems
Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation
With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume.
ICFVR 2017: 3rd International Competition on Finger Vein Recognition
The development of finger vein recognition algorithms heavily depends on large-scale real-world data sets.
Learning to Design Games: Strategic Environments in Reinforcement Learning
In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment.
Empirically Grounded Agent-Based Models of Innovation Diffusion: A Critical Review
Innovation diffusion has been studied extensively in a variety of disciplines, including sociology, economics, marketing, ecology, and computer science.
