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Tag: rl

  • Reinforcement Learning for Control Systems with Time Delays: A Comprehensive Survey

    Reinforcement Learning for Control Systems with Time Delays: A Comprehensive Survey arXiv:2602.00399v1 Announce Type: new Abstract: In the last decade, Reinforcement Learning (RL) has achieved remarkable success in the control and decision-making of complex dynamical systems. However, most RL algorithms rely on the Markov Decision Process assumption, which is violated in practical cyber-physical systems affected…

  • Heuristics for Combinatorial Optimization via Value-based Reinforcement Learning: A Unified Framework and Analysis

    Heuristics for Combinatorial Optimization via Value-based Reinforcement Learning: A Unified Framework and Analysis arXiv:2512.08601v1 Announce Type: new Abstract: Since the 1990s, considerable empirical work has been carried out to train statistical models, such as neural networks (NNs), as learned heuristics for combinatorial optimization (CO) problems. When successful, such an approach eliminates the need for experts…

  • Statistical and Algorithmic Foundations of Reinforcement Learning

    Statistical and Algorithmic Foundations of Reinforcement Learning arXiv:2507.14444v1 Announce Type: new Abstract: As a paradigm for sequential decision making in unknown environments, reinforcement learning (RL) has received a flurry of attention in recent years. However, the explosion of model complexity in emerging applications and the presence of nonconvexity exacerbate the challenge of achieving efficient RL…

  • Nuclear Microreactor Control with Deep Reinforcement Learning

    Nuclear Microreactor Control with Deep Reinforcement Learning arXiv:2504.00156v1 Announce Type: cross Abstract: The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for…

  • Statistical Inference in Reinforcement Learning: A Selective Survey

    Statistical Inference in Reinforcement Learning: A Selective Survey arXiv:2502.16195v1 Announce Type: new Abstract: Reinforcement learning (RL) is concerned with how intelligence agents take actions in a given environment to maximize the cumulative reward they receive. In healthcare, applying RL algorithms could assist patients in improving their health status. In ride-sharing platforms, applying RL algorithms could…

  • Understanding the Mathematics of PPO in Reinforcement Learning

    Understanding the Mathematics of PPO in Reinforcement Learning Deep dive into RL with PPO for beginners Photo by ThisisEngineering on Unsplash Introduction Reinforcement Learning (RL) is a branch of Artificial Intelligence that enables agents to learn how to interact with their environment. These agents, which range from robots to software features or autonomous systems, learn through…

  • Navigating Soft Actor-Critic Reinforcement Learning

    Navigating Soft Actor-Critic Reinforcement Learning Understanding the theory and implementation of SAC RL in the context of Bioengineering Image generated by the author using ChatGPT-4o Introduction The research domain of Reinforcement Learning (RL) has evolved greatly over the past years. The use of deep reinforcement learning methods such as Proximal Policy Optimisation (PPO) (Schulman, 2017)…