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Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods
To our best knowledge, this is the first theoretical guarantee on fictitious discount algorithms for the episodic reinforcement learning of finite-time-horizon MDPs, which also leads to the (first) global convergence of policy gradient methods for finite-time-horizon episodic reinforcement learning.
Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controls
We study finite-time horizon continuous-time linear-convex reinforcement learning problems in an episodic setting.
Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon
We study finite-time horizon continuous-time linear-quadratic reinforcement learning problems in an episodic setting, where both the state and control coefficients are unknown to the controller.
A General Framework for Learning Mean-Field Games
This paper presents a general mean-field game (GMFG) framework for simultaneous learning and decision-making in stochastic games with a large population.
