Walking and Survival AI Using Reinforcement Learning - Simulation

This research paper presents a novel approach to training an AI agent for walking and survival tasks using reinforcement learning (RL) techniques. The primary research question addressed in this study is how to develop an AI system capable of autonomously navigating diverse terrains and environments while ensuring survival through adaptive decision-making. To investigate this question, we employ RL algorithms, specifically deep Q-networks (DQN) and proximal policy optimization (PPO), to train an AI agent in simulated environments that mimic real-world challenges. Our methodology involves designing a virtual environment where the AI agent learns to walk and make survival-related decisions through trial and error. The agent receives rewards or penalties based on its actions, encouraging the development of strategies that optimize both locomotion and survival skills. We evaluate the performance of our approach through extensive experimentation, testing the AI agent's adaptability to various terrains, obstacles, and survival scenarios.