Testing the Invariance of Skills and Strategies Developed by Artificial Agents under Different Sensory Modalities
| dc.contributor.advisor | Menore Tekeba (PhD) | |
| dc.contributor.author | Meseret Gebremichael | |
| dc.date.accessioned | 2025-04-25T08:27:31Z | |
| dc.date.available | 2025-04-25T08:27:31Z | |
| dc.date.issued | 2024-06 | |
| dc.description.abstract | Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals by developing artificial intelligence agents. Artificial Intelligence (AI) agent must be aware of the external environment to understand or to execute tasks. The interaction between an agent and the environment is achieved by the aid of input sensory modality (image, sound, lidar or other input modality) using metrics reward sizes they collect during reinforcement learning. To do so, the we used two algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) for the experiments. PPO is a reinforcement learning algorithm that is used to train agents to perform tasks in environments through trial and error. It is designed to optimize policies, which are the strategies or behaviors that the agents use to make decisions. PPO aims to find the optimal policy by iteratively updating the parameters based on the collected experiences from interactions with the environment. SAC is a reinforcement learning algorithm used for training agents in environments with continuous action spaces. It is an extension of the actor-critic framework that combines the advantages of both policy optimization and value estimation methods. During the experiment researcher developed the agents and the environment model in the project and selected the appropriate RL method, implemented and designed the metrics of the learning performance of the agents depending on their nature. We demonstrate agents' varied training performances under different sensory modalities, with optimal outcomes observed when combining multiple modalities. Despite these differences, the study underscores agents' adaptability across sensory inputs, advancing our understanding of cross-modal learning in AI. Also, the researcher has shown the invariance of sensory modality results on the learning skills and strategies of an agent and developed simple game like reaching the goal with different input sensory information to test the agent skill and strategies under different perceptual modalities. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/5380 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Reinforcement Learning | |
| dc.subject | Proximal Policy Optimization | |
| dc.subject | Soft Actor-Critic | |
| dc.title | Testing the Invariance of Skills and Strategies Developed by Artificial Agents under Different Sensory Modalities | |
| dc.type | Thesis |