Temel Kavramlar
Reinforcement learning on high-dimensional and complex problems relies on abstraction for improved efficiency and generalization.
İstatistikler
Reinforcement learning on high-dimensional and complex problems relies on abstraction for improved efficiency and generalization.
Our policy gradient results allow for leveraging approximate symmetries of the environment for policy optimization.
Our method’s ability to utilize MDP homomorphisms for representation learning leads to improved performance.
Alıntılar
"Our method’s ability to utilize MDP homomorphisms for representation learning leads to improved performance."
"Our policy gradient results allow for leveraging approximate symmetries of the environment for policy optimization."