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학술대회 Multi-agent Reinforcement Learning in a Large Scale Environment via Supervisory Network and Curriculum Learning
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도승원, 이창은
International Conference on Control, Automation and Systems (ICCAS) 2021, pp.207-210
Multi-agent reinforcement learning is essential for learning optimal policy for collaboration and competition environments. However, as the action space of the agent increases, the number of state-action pairs which have to be explored increases exponentially. As a result, increasing search space causes difficulty to converge the learning. To solve this problem, we propose a supervisory network. To achieve the global goal, the supervisory network creates a sub-goal and assigns the goals to the agents so that the agents can effectively learn the optimal policy with a small action space. In addition, we adapt the curriculum learning method to learn a large-scale environment. As a consequence, the agent can explore the environment in which the complexity increases gradually. Although a baseline network was learned in the same environment to compare with our model, the baseline fails to learn an optimal policy while our model successes to learn in the large-scale environment.
KSP 제안 키워드
Action space, Baseline network, Collaboration and Competition, Curriculum learning, Large scale environment, Learning methods, Optimal policy, Reinforcement Learning(RL), Search Space, Sub-goal, multi-agent reinforcement learning