• behaviour characterization
• gaussian mixture models
• multi-task reinforcement learning
• reinforcement learning
• transfer learning

This thesis proposes a novel Multi-Task Reinforcement Learning (MTRL) framework that facilitates inter-environment policy transfer by leveraging state-based behavioural characterizations. Our method encodes policies through supervector representations derived from Gaussian Mixture Models (GMMs) trained on visited states. By embedding policies into a shared latent space, we enable similarity-based policy selection, guiding learning in new tasks based on previously acquired knowledge. This approach is evaluated across environments from the Highway-Env suite, aiming to demonstrate its effectiveness in accelerating adaptation, improving generalization, and identifying transferable skills between source and target tasks.