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@article{198267,
        author = {Shahid Mulani and Rudra Bundele and Shruti Tiwari and Neha Shinde},
        title = {Reinforcement Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {8046-8050},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198267},
        abstract = {Reinforcement Learning (RL) is a paradigm of machine learning wherein an agent learns to make sequential decisions by interacting with an environment to maximize cumulative rewards. This paper presents a comprehensive survey of RL, covering its theoretical foundations in Markov Decision Processes, core algorithms including Q-Learning, SARSA, and Monte Carlo methods, and advanced deep RL frameworks such as Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Actor-Critic architectures. Applications across robotics, game-playing, healthcare, and autonomous systems are reviewed, alongside open challenges including sample inefficiency, reward specification, and safe exploration. The paper concludes with directions for future research bridging RL with large foundation models.},
        keywords = {Reinforcement Learning, Deep Q-Networks, Markov Decision Processes, Policy Gradient Methods, Actor-Critic, Reward Shaping, Autonomous Systems, Machine Learning.},
        month = {April},
        }