Search Results for author:
Found 14 papers, 10 papers with code
LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments
Recent advancements in large language models (LLMs) have revealed their potential for achieving autonomous agents possessing human-level intelligence.
AutoSAT: Automatically Optimize SAT Solvers via Large Language Models
Heuristics are crucial in SAT solvers, while no heuristic rules are suitable for all problem instances.
OpenRL: A Unified Reinforcement Learning Framework
We present OpenRL, an advanced reinforcement learning (RL) framework designed to accommodate a diverse array of tasks, from single-agent challenges to complex multi-agent systems.
Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models
The rise of deepfake images, especially of well-known personalities, poses a serious threat to the dissemination of authentic information.
Diverse Policies Converge in Reward-free Markov Decision Processe
Under such a framework, we also propose a provably efficient diversity reinforcement learning algorithm.
SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
The Swift module is a small encoder-decoder LM fine-tuned on the oracle agent's action trajectories, while the Sage module employs LLMs such as GPT-4 for subgoal planning and grounding.
TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play
Multi-agent football poses an unsolved challenge in AI research.
Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation
Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge by introducing a hierarchical structure to decompose the search space, where the low-level policy predicts primitive actions in the guidance of the goals derived from the high-level policy.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
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DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization
We propose Diversity-Guided Policy Optimization (DGPO), an on-policy algorithm that discovers multiple strategies for solving a given task.
TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations
To the best of our knowledge, Tikick is the first learning-based AI system that can take over the multi-agent Google Research Football full game, while previous work could either control a single agent or experiment on toy academic scenarios.
Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization
In this work, we propose a new algorithm for circuit routing, named Ranking Cost, which innovatively combines search-based methods (i. e., A* algorithm) and learning-based methods (i. e., Evolution Strategies) to form an efficient and trainable router.
Ranking Cost: One-Stage Circuit Routing by Directly Optimizing Global Objective Function
In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths to achieve the global object.
SVQN: Sequential Variational Soft Q-Learning Networks
Partially Observable Markov Decision Processes (POMDPs) are popular and flexible models for real-world decision-making applications that demand the information from past observations to make optimal decisions.
Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters
Such "in-the-tail" data is notoriously hard to observe, making both training and testing difficult.
