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@article{206278,
        author = {Sachin Shankarrao Damre and Dr. Sushil Venkatesh Kulkarni},
        title = {Comparative Study of Curriculum Learning and Behavior Cloning in Reinforcement Learning-Based Multi-Agent Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {844-848},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206278},
        abstract = {Reinforcement Learning (RL) has become one of the most influential paradigms in Artificial Intelligence for developing autonomous agents capable of learning optimal behavior through interaction with dynamic environments. Recent advancements in Deep Reinforcement Learning (DRL) have enabled intelligent systems to solve complex decision-making tasks in gaming, robotics, autonomous navigation, and industrial automation. This review paper presents a comprehensive survey of RL, Deep RL, Curriculum Learning, Behavior Cloning, Generative Adversarial Imitation Learning (GAIL), and Unity ML-Agents. Particular emphasis is placed on intelligent multi-agent game environments where cooperation, competition, and coordination significantly increase learning complexity. The paper provides a comparative analysis of Curriculum Learning and Behavior Cloning, identifies current research trends, highlights limitations in existing literature, and discusses future directions. The study reveals that combining curriculum-based training with imitation learning techniques offers promising opportunities for developing scalable and robust intelligent agents in complex gaming environments.},
        keywords = {Reinforcement Learning, Deep Reinforcement Learning, Curriculum Learning, Behavior Cloning, GAIL, PPO, Unity ML-Agents, Multi-Agent Systems, Game AI, Intelligent Agents.},
        month = {July},
        }