Autonomous Systems and Decision-making Lab. 자율 시스템 및 의사결정 연구실

Research Vision - developing autonomous agents and improving their sequential decision-making capabilities. In reinforcement learning, an agent learns a policy to maximize the return (i.e., the cumulative reward) by interacting with the environment. However, when the state and action spaces are large, training a policy from scratch often takes a long time. Various studies have been conducted to enhance sample efficiency during training and expedite learning. Sample-efficient training enables the agent to achieve a better policy with the same number of training samples. Imitation learning enables an agent to learn a complex policy from expert demonstrations. This method is effective for tasks where the reward signal is sparse or the reward design is not straightforward. In this regard, behavioral cloning has been widely adopted for robot tasks and shown its effectiveness in a variety of tasks (See the following videos). However, limited generalizability in OOD (out-of-distribution) conditions still remains a key challenge. In multi-agent reinforcement learning, learning cooperative policies for agents still remains a challenging problem. Moreover, when communication is not allowed among agents, each agent must make a decision based solely on its partial observation, yielding a problem class as DecPOMDP (Decentralized Partially Observable Markov Decision Process). Training multiple agents often experiences non-stationarity and local convergence, thus it often requires a long training time. To address these limitations, MARL requires a more sophisticated learning strategy. Learning concise and informative representations is critical for efficient learning, as it captures key features important for completing a given task. Similarly, using semantically similar states during