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    Area of Science:

    • Artificial Intelligence
    • Robotics
    • Computer Vision

    Background:

    • Deep reinforcement learning (DRL) shows promise in simulations but struggles with real-world application due to environmental limitations.
    • Existing simulation environments lack the visual fidelity, complexity, and task diversity needed for advanced DRL research.
    • Bridging the gap between simulated and real-world DRL performance requires more sophisticated and realistic training grounds.

    Purpose of the Study:

    • To develop a novel, realistic 3D simulation environment for advancing deep reinforcement learning.
    • To introduce a new DRL algorithm designed to handle complex, open-world scenarios.
    • To evaluate the effectiveness of the proposed environment and algorithm in enabling human-level decision-making for DRL agents.

    Main Methods:

    • Development of Unreal BattleGround (UBG), a 3D open-world first-person shooter game using the Unreal Engine, featuring variable complexity, random scenes, and diverse tasks.
    • Proposal of the object-aware hierarchically proximal policy optimization (OaH-PPO) method, incorporating a two-level hierarchy for option control and subtask mastery.
    • Integration of an object-aware module for depth detection, potential-based intrinsic reward shaping for exploration, and annealing imitation learning for guided initialization.

    Main Results:

    • The UBG environment demonstrates broad applicability for training and testing DRL agents in complex scenarios.
    • The OaH-PPO method significantly enhances DRL agent performance within the UBG benchmark.
    • Experimental results validate the effectiveness of the proposed hierarchical approach and its supporting modules.

    Conclusions:

    • Unreal BattleGround (UBG) provides a robust and realistic platform for overcoming DRL limitations in complex environments.
    • The object-aware hierarchically proximal policy optimization (OaH-PPO) method offers an effective solution for improving DRL agent capabilities.
    • The developed UBG environment and OaH-PPO algorithm represent a significant step towards deploying DRL in real-world applications.