Improving single and multi-agent deep reinforcement learning methods - Gupta, T (2023)
Reinforcement Learning (RL) is a framework where an agent learns to make decisions using data-driven feedback from interactions with the environment in the form of rewards or penalties for actions. Deep RL integrates deep learning with RL, harnessing the power of deep neural networks to process complex, high-dimensional data. Using the framework of deep RL, our machine learning research community has achieved tremendous progress in enabling machines to make sequential decisions over long time horizons. These advances include attaining super-human performance in Atari [Mnih et al., 2015], mastering the game of Go, beating the human world champion [Silver et al., 2017], providing robust recommendation systems [GomezUribe and Hunt, 2015, Singh et al., 2021]. This thesis focuses on identifying some key challenges that impede the learning of RL agents within their specific environments and improving the methods leading to better performance of agents, improved sample efficiency, and generalizability of learned agent policies.