89 citations · 216 across the 11 of their papers we have counts for
5 papers · 1 filter
Provably Efficient Exploration in Policy Optimization
Qi Cai, Zhuoran Yang, Chi Jin +1
While policy-based reinforcement learning (RL) achieves tremendous successes in practice, it is significantly less understood in theory, especially compared with value-based RL. In…
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence
Lingxiao Wang, Qi Cai, Zhuoran Yang +1
Policy gradient methods with actor-critic schemes demonstrate tremendous empirical successes, especially when the actors and critics are parameterized by neural networks. However,…
Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy
Boyi Liu, Qi Cai, Zhuoran Yang +1
Proximal policy optimization and trust region policy optimization (PPO and TRPO) with actor and critic parametrized by neural networks achieve significant empirical success in deep…
Neural Temporal-Difference and Q-Learning Provably Converge to Global Optima
Qi Cai, Zhuoran Yang, Jason D. Lee +1
Temporal-difference learning (TD), coupled with neural networks, is among the most fundamental building blocks of deep reinforcement learning. However, due to the nonlinearity in v…
On the Global Convergence of Imitation Learning: A Case for Linear Quadratic Regulator
Qi Cai, Mingyi Hong, Yongxin Chen +1
We study the global convergence of generative adversarial imitation learning for linear quadratic regulators, which is posed as minimax optimization. To address the challenges aris…