Maneuver Decision-Making Through Proximal Policy Optimization And Monte Carlo Tree Search
This is an incremental improvement for air combat simulation, addressing a specific bottleneck in reinforcement learning training.
The paper tackles the problem of maneuver decision-making in air combat, which original reinforcement learning algorithms struggle with due to random actions in early training, by proposing a method combining proximal policy optimization and Monte Carlo tree search to improve training performance, with simulation experiments showing the method enables agents to make state-dependent decisions.
Maneuver decision-making can be regarded as a Markov decision process and can be address by reinforcement learning. However, original reinforcement learning algorithms can hardly solve the maneuvering decision-making problem. One reason is that agents use random actions in the early stages of training, which makes it difficult to get rewards and learn how to make effective decisions. To address this issue, a method based on proximal policy optimization and Monte Carlo tree search is proposed. The method uses proximal policy optimization to train the agent, and regards the results of air combat as targets to train the value network. Then, based on the value network and the visit count of each node, Monte Carlo tree search is used to find the actions with more expected returns than random actions, which can improve the training performance. The ablation studies and simulation experiments indicate that agents trained by the proposed method can make different decisions according to different states, which demonstrates that the method can solve the maneuvering decision problem that the original reinforcement learning algorithm cannot solve.