Neuroevolution vs Reinforcement Learning for Training Non Player Characters in Games: The Case of a Self Driving Car

Kovalský, Kristián; Palamas, George

doi:10.1007/978-3-030-76426-5_13

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…The use of RL algorithms to train intelligent self-driving vehicles can also extend to the gaming industry. Game developers can use these algorithms to create more intelligent and lifelike non-playable characters (NPCs) [67], which can interact with players in more engaging ways.…”

Section: Possible Applicationsmentioning

confidence: 99%

Deep Reinforcement Learning for Autonomous Driving in Amazon Web Services DeepRacer

Petryshyn,

Postupaiev,

Ben Bari

et al. 2024

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The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is an underexplored research area. Amazon Web Services (AWS) DeepRacer emerges as a powerful infrastructure for engineering and analysing autonomous models, providing a robust foundation for addressing these complexities. This research investigates the feasibility of training end-to-end self-driving models focused on obstacle avoidance using reinforcement learning on the AWS DeepRacer autonomous race car platform. A comprehensive literature review of autonomous driving methodologies and machine learning model architectures is conducted, with a particular focus on object avoidance, followed by hands-on experimentation and the analysis of training data. Furthermore, the impact of sensor choice, reward function, action spaces, and training time on the autonomous obstacle avoidance task are compared. The results of the best configuration experiment demonstrate a significant improvement in obstacle avoidance performance compared to the baseline configuration, with a 95.8% decrease in collision rate, while taking about 79% less time to complete the trial circuit.

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Section: Possible Applicationsmentioning

confidence: 99%

Deep Reinforcement Learning for Autonomous Driving in Amazon Web Services DeepRacer

Petryshyn,

Postupaiev,

Ben Bari

et al. 2024

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“…The use of RL algorithms to train intelligent agents in simulated environments can also have applications in the field of gaming. Game developers can use these algorithms to create more intelligent and realistic non-player characters (NPCs) [53,54] that can interact with players in more complex ways. Overall, the use of the Unity ML-Agents toolkit to train intelligent agents using RL algorithms has the potential to revolutionize several industries, including autonomous vehicles, robotics and gaming.…”

Section: Possible Applicationsmentioning

confidence: 99%

Simulated Autonomous Driving Using Reinforcement Learning: A Comparative Study on Unity’s ML-Agents Framework

Savid,

Mahmoudi,

Maskeliūnas

et al. 2023

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Advancements in artificial intelligence are leading researchers to find use cases that were not as straightforward to solve in the past. The use case of simulated autonomous driving has been known as a notoriously difficult task to automate, but advancements in the field of reinforcement learning have made it possible to reach satisfactory results. In this paper, we explore the use of the Unity ML-Agents toolkit to train intelligent agents to navigate a racing track in a simulated environment using RL algorithms. The paper compares the performance of several different RL algorithms and configurations on the task of training kart agents to successfully traverse a racing track and identifies the most effective approach for training kart agents to navigate a racing track and avoid obstacles in that track. The best results, value loss of 0.0013 and a cumulative reward of 0.761, were yielded using the Proximal Policy Optimization algorithm. After successfully choosing a model and algorithm that can traverse the track with ease, different objects were added to the track and another model (which used behavioral cloning as a pre-training option) was trained to avoid such obstacles. The aforementioned model resulted in a value loss of 0.001 and a cumulative reward of 0.068, proving that behavioral cloning can help achieve satisfactory results where the in game agents are able to avoid obstacles more efficiently and complete the track with human-like performance, allowing for a deployment of intelligent agents in racing simulators.

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Comparative studies of evolutionary methods and RL for learning behavior of virtual creatures

Saito

Nishimura

Oka

2022

2022 IEEE Symposium Series on Computational Intelligence (SSCI)

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Neuroevolution vs Reinforcement Learning for Training Non Player Characters in Games: The Case of a Self Driving Car

Cited by 4 publications

References 23 publications

Deep Reinforcement Learning for Autonomous Driving in Amazon Web Services DeepRacer

Deep Reinforcement Learning for Autonomous Driving in Amazon Web Services DeepRacer

Simulated Autonomous Driving Using Reinforcement Learning: A Comparative Study on Unity’s ML-Agents Framework

Comparative studies of evolutionary methods and RL for learning behavior of virtual creatures

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