Junhyuk Kim  Junoh Kim  Kyungeun Cho

10.5626/JOK.2025.52.4.293

deep RL policy-based RL algorithm simulated-annealing dynamic dense reward

Nowadays, reinforcement learning is being studied and utilized in various fields, including autonomous driving, robotics, and gaming. The goal of reinforcement learning is to find the optimal policy for an agent to interact with its environment. Depending on the environment and the specific problem, either a policy-based algorithm or a value-based algorithm is selected for use. Policy-based algorithms can effectively learn in continuous and high-dimensional action spaces, but they face challenges such as the influence of learning rate parameters on the learning process and increased difficulty in converging to an optimized policy in complex environments. To address these issues, this paper proposes a behavior selection technique and a dynamic dense reward design based on a simulated annealing algorithm. The proposed method is applied to two different environments, and experimental results show that the policy-based reinforcement learning algorithms utilizing this method outperform the standard reinforcement learning algorithms.