The Ladder in Chaos: Improving Policy Learning by Harnessing the Parameter Evolving Path in A Low-dimensional Space Hongyao Tang

Deep Reinforcement Learning (DRL) is far from well understood, although its great potential has been demonstrated with a lot of achievements in different practical problems [Badia et al., 2020, Shah et al., 2022, Fawzi et al., 2022, Degrave et al., 2022, OpenAI, 2022]. Consistent efforts are made to gain a better understanding of the learning dynamics of RL agents.