AUGMENTING SELF-LEARNING AGENT IN FIRST-PERSON SHOOTER GAME USING REINFORCEMENT LEARNING

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dc.contributor.authorSINGH, SAMRAT
dc.contributor.authorNEUPANE, SKEIN
dc.contributor.authorPANDEY, SUSHANT
dc.contributor.authorJOSHI, YACHU RAJA
dc.date.accessioned2023-07-30T07:04:03Z
dc.date.available2023-07-30T07:04:03Z
dc.date.issued2023-04-30
dc.descriptionReinforcement Learning(RL) is a powerful machine learning technique in which an agent learns to solve a problem by interacting with its environment. Through a system of rewards and punishments, agents learn to make decisions that maximize rewards and minimize negative consequences. RL is rapidly becoming a prominent area of research, with a growing number of applications in a wide range of fields .en_US
dc.description.abstractThis group project highlights the effectiveness of utilizing reinforcement learning (RL) along with the Proximal Policy Optimization (PPO) algorithm to train an agent to play aWolfenstein3Dlike game with multiple levels. The agent exhibited exceptional performance in relation to reward, time efficiency, and overall effectiveness. An in-depth analysis of its performance indicated marked enhancements in the reward curves, strategic navigation throughout the game levels, and expeditious completion of each level. The study highlights the potential of RL and PPO for training agents in complex video games with multiple levels, as well as in other applications such as agent-based modeling and machine learning.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14540/18814
dc.language.isoenen_US
dc.publisherI.O.E. Pulchowk Campusen_US
dc.subjectReinforcement Learning,en_US
dc.subjectDeep Reinforcement Learning,en_US
dc.subjectCurriculum Learningen_US
dc.titleAUGMENTING SELF-LEARNING AGENT IN FIRST-PERSON SHOOTER GAME USING REINFORCEMENT LEARNINGen_US
dc.typeReporten_US
local.academic.levelBacheloren_US
local.affiliatedinstitute.titlePulchowk Campusen_US
local.institute.titleInstitute of Engineeringen_US
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