Integration Of Reinforcement Learning For Enhanced Gaming Physics: A Study Of Ragdoll Behavior And Dynamic Terrain Navigation

Abstract

Reinforcement learning (RL) has transformed the world of artificial intelligence in terms of enabling agents to learn complex behaviors through interaction with their environments. In the gaming domain, RL provides the possibility to create lifelike agents who can make dynamic decisions and learn to adapt to demanding terrains. However, even with significant progress in the field, designing and training RL agents for more complex, interactive environments are a challenging task, especially when lifelike behavior with robust performance is desired. This paper discusses developing and training custom RL agents on Unity ML-Agents with the PPO algorithm. The main challenge is to produce an agent that can navigate dynamic terrains and adapt to varied situations while being computationally efficient. Most frameworks require massive fine-tuning, which consumes a lot of time and resources. To address the above problem, we suggest an integrated curriculum learning approach coupled with dynamic terrain generation and tailored reward structures. The Unity ML-Agents framework is used for smooth environment creation and simulation, and the PPO algorithm ensures stable and efficient learning of policies. Experimental results show marked improvements in the adaptability and performance metrics of agents, thereby signifying the efficacy of our proposed approach. This contribution advances RL applications for games, thereby opening a route to more immersive and intelligent virtual environments.