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reinforcement learning

2,613 papers

#reinforcement learning Open access Aug 2026

Multi-Agent Reinforcement Learning with Intrinsic Motivation and Cooperative Reward Shaping

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...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Aug 2026

Probabilistic Programming for Reinforcement Learning with Exploration

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...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Aug 2026

Relational Reinforcement Learning with Graph-Based Reward Shaping

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...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Aug 2026

Autonomous Enterprise Platforms: A Framework for AI-Guided Decision Loops, Predictive Intelligence, and Continuous Organizational Adaptation

Enterprise platforms are evolving from systems that primarily record and analyze business operations into intelligent environments capable of predicting outcomes, recommending interventions, executing decisions, and learning from their consequences. This article proposes a conceptual Autonomous Enterprise Platform (AEP...

Shekar Vollem · 0 citations
#reinforcement learning Open access Aug 2026

Neuro-Symbolic Logic Programming with Reinforcement Learning

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...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Aug 2026

Based on Multi-Agent Reinforcement Learning for System Optimization (MARSL)

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...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Aug 2026

Based on Multi-Modal Context for Dynamic Program Generation

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...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Aug 2026

Chaos Dynamic Optimization Algorithm via Reinforcement Learning Fusion

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...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Aug 2026

Dynamic Topo-Semantic Network Learning (DTSNL)

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...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Aug 2026

Relational Reinforcement Learning with Graph-Based Reward Shaping

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...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Aug 2026

Adaptive Bayesian Optimization for Reinforcement Learning Reward Shaping

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...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Aug 2026

Based on Multi-modal Data Fusion for Subconscious Decision Simulation

This paper presents a novel approach to simulating subconscious decision-making processes by leveraging multi-modal data fusion. The core idea is to construct a computational model capable of mirroring the complexities of human subconscious decision-making, moving beyond traditional behavioral analysis. We employ a gra...

Jincheng Zhang · 0 citations

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Microsoft Research Blog Nov 3, 2026

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Google DeepMind Blog Nov 3, 2026

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MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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