arXiv:2402.10290v1 Announce Type: new
Abstract: The project’s aim is to create an AI agent capable of selecting good actions in a game-playing domain called Battlespace. Sequential domains like Battlespace are important testbeds for planning problems, as such, the Department of Defense uses such domains for wargaming exercises. The agents we developed combine Monte Carlo Tree Search (MCTS) and Deep Q-Network (DQN) techniques in an effort to navigate the game environment, avoid obstacles, interact with adversaries, and capture the flag. This paper will focus on the encoding techniques we explored to present complex structured data stored in a Python class, a necessary precursor to an agent.

Analysis of AI Agent Development for Battlespace

In the field of AI research, the development of agents capable of making intelligent decisions in complex game-playing domains has been a significant area of focus. The aim of the project discussed in this paper is to create an AI agent specifically designed for the Battlespace domain. Battlespace is a sequential domain that has significant relevance in planning problems and is widely used by the Department of Defense for wargaming exercises. By developing agents that can navigate the game environment, avoid obstacles, interact with adversaries, and capture the flag, the project aims to contribute to the advancements in agent-based decision-making.

The uniqueness of this project lies in the combination of Monte Carlo Tree Search (MCTS) and Deep Q-Network (DQN) techniques. MCTS is a search algorithm that explores the game tree by sampling possible actions and estimating their values based on simulations. DQN, on the other hand, is a type of reinforcement learning technique that utilizes neural networks to approximate action-value functions. By combining these two approaches, the agents in this project can leverage the strengths of both methods, resulting in more effective decision-making.

One of the key challenges in developing these AI agents is encoding complex structured data stored in a Python class. This encoding technique is crucial as it transforms raw data into a format that can be effectively processed and utilized by the agents. The paper focuses on exploring different encoding techniques to represent this structured data, ensuring that the agents have access to all the relevant information necessary for decision-making.

It is worth noting that this project encompasses various disciplines and technologies. The utilization of MCTS and DQN combines concepts from both artificial intelligence and machine learning. Additionally, the incorporation of Python programming language and structured data encoding techniques showcases the importance of software engineering and data manipulation skills. Overall, this multi-disciplinary nature of the project highlights the collaborative efforts required to develop cutting-edge AI systems.

Looking ahead, there are several potential areas of improvement and expansion for this project. Firstly, further enhancements can be made to the agents’ decision-making capabilities by exploring advanced variations of MCTS and DQN algorithms. This could involve incorporating techniques such as Upper Confidence Bound (UCB) for action selection in MCTS or prioritized experience replay in DQN.

Furthermore, the project can benefit from incorporating other reinforcement learning algorithms to compare their performance against MCTS-DQN hybrid agents. Algorithms like Proximal Policy Optimization (PPO) or Asynchronous Advantage Actor-Critic (A3C) could be explored to provide additional insights into the agent’s decision-making efficiency.

In conclusion, the development of AI agents for game-playing domains like Battlespace showcases the advancements in AI and machine learning. By combining techniques such as MCTS and DQN, and addressing the challenges of encoding complex structured data, this project contributes to the field of agent-based decision-making. As technology continues to progress, further research and improvements in this area will undoubtedly pave the way for more intelligent and capable AI agents.

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