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Research Topic Ideas in Imitation Learning

Research Topic Ideas in Imitation Learning

Latest Research Topic Ideas in Imitation Learning

   Imitation Learning (IL), or learning from demonstration, learns to mimic human behavior from the demonstrations for a given task. Imitation learning is the process of learning the feature representations from demonstration sources. Intimation learning provides similar behavior in the demonstration, and demonstrations comprise both state and action sequences. An essential component of IL is the Markov decision process(MDP).
    The significance of IL is a better demonstration of real-world application and efficiency to train a policy. Compared to RL, IL is more efficient, accessible, and human-interactive. The two main categories of imitation learning are behavioral cloning and inverse reinforcement learning. Behavioral cloning(BC) utilizes supervised learning to learn an imitation policy, whereas Inverse Reinforcement Learning (IRL) uses reinforcement learning with inferred reward function to learn an imitation policy.
    Other methods involved in IL are Generative Adversarial Imitation Learning (GAIL) and Imitation From Observation(IFO). The most popular application fields of imitation learning are autonomous vehicles, assistive robots, humanoid robots, electronic games, and human-computer interaction. Future scopes of imitation learning are general feature representation and task learning, multi-agent imitation, learning diverse behavior, combining IL with transfer learning, and importance sampling and agent memory.