The reinforcement learning of an autonomous agent, Case study: Taxi Game
| dc.contributor.author | Boudouha Islem Hoggas Salah Eddine | |
| dc.date.accessioned | 2025-02-10T08:17:36Z | |
| dc.date.available | 2025-02-10T08:17:36Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Reinforcement learning (RL) is a powerful machine learning technique that enables autonomous agents to learn and make decisions by interacting with their environment. It is inspired by the behavioural psychology concept of learning through trial-and-error, where an agent takes actions, receives rewards or penalties, and adjusts its behaviour accordingly to achieve specific goals. In this thesis, we focus on the mechanisms, techniques, and applications of this exciting discipline, We apply the Q-learning algorithm wich is a reinforcement learning algorithm that seeks to find the best possible next action given its current state, in order to maximise the reward it receives from the enviroment Taxi game wich is one of many environments available on OpenAI Gym, The aim of this algorithm is to make sure the taxi can get to the passenger, pick him up and bring him to the drop-off location in the fastest way possible, The core concept of Q-learning revolves around the notion of a Q-table, a data structure that stores the estimated values of taking a specific action in a particular state. As the agent (taxi) interacts with the environment, it continuously updates the Q-table based on the rewards it receives. This iterative process enables the agent to gradually learn which actions lead to favorable outcomes and which ones should be avoided. | |
| dc.identifier.uri | http://dspace.univ-khenchela.dz:4000/handle/123456789/7820 | |
| dc.language.iso | en | |
| dc.title | The reinforcement learning of an autonomous agent, Case study: Taxi Game | |
| dc.type | Thesis |
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