127 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This paper presents a novel reinforcement learning (RL) algorithm, Dynamic Kernel Learning (DKL), designed to enhance the performance of RL agents in complex environments. DKL addresses the limitations of traditional RL methods that often rely on fixed kernel functions for value estimation. The core innovation lies in...
This paper presents a novel reinforcement learning (RL) algorithm, Dynamic Kernel Learning (DKL), designed to enhance the performance of RL agents in complex environments. DKL addresses the limitations of traditional RL methods that often rely on fixed kernel functions for value estimation. The core innovation lies in...
Multi-Agent Reinforcement Learning (MARL) offers a promising approach to tackling complex cooperative tasks. However, existing MARL algorithms often fail to achieve robust coordination and cooperation, primarily due to difficulties in learning effective joint policies and the lack of mechanisms to encourage collaborati...
Reinforcement learning (RL) faces significant challenges in achieving optimal performance due to the inherent difficulty in balancing exploration and exploitation. This paper proposes a novel framework leveraging probabilistic programming (PP) to address this problem. The core idea is to represent an RL agent as a prob...
This paper proposes a novel approach to reinforcement learning (RL) that leverages relational reasoning through the incorporation of graph-based reward shaping. Traditional RL algorithms often struggle in environments exhibiting complex relationships between entities, leading to inefficient learning and suboptimal poli...
This paper proposes a novel approach to artificial intelligence—Neuro-Symbolic Logic Programming with Reinforcement Learning—designed to address the limitations of current AI techniques. The core idea is to integrate the pattern recognition capabilities of neural networks with the reasoning and explainability offered b...
This paper proposes a novel approach to system optimization leveraging Multi-Agent Reinforcement Learning (MARL). The core concept is to decompose complex systems into multiple intelligent agents, each responsible for controlling a specific subsystem. These agents then collaboratively learn optimal control strategies t...
This paper introduces a novel approach to dynamic program generation, termed "Based on Multi-Modal Context for Dynamic Program Generation," which moves beyond static template-based code modification by leveraging a combination of code, natural language descriptions, execution logs, and user interaction data. The core o...
This paper investigates the integration of chaos dynamic optimization algorithms with reinforcement learning to develop a robust and adaptable system for complex systems, particularly within control and design domains. Traditional optimization methods often rely on handcrafted parameters, limiting flexibility. This res...
This paper proposes a novel approach to distributed system management called Dynamic Topo-Semantic Network Learning (DTSNL). DTSNL leverages reinforcement learning to enable systems to automatically discover and adapt to changes in underlying hardware and software topology. The core idea is to deploy agents, each respo...
This paper proposes a novel approach to reinforcement learning (RL) that leverages relational reasoning through the incorporation of graph-based reward shaping. Traditional RL algorithms often struggle in environments exhibiting complex relationships between entities, leading to inefficient learning and suboptimal poli...
Reward shaping is a critical technique in reinforcement learning (RL) that aims to accelerate learning by providing the agent with informative rewards. However, designing effective reward shaping functions can be a challenging and often tedious process, requiring domain expertise and extensive manual tuning. This paper...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
