Finite-time convergence and sample complexity of actor-critic multi-objective reinforcement learning
Reinforcement learning with multiple, potentially conflicting objectives is pervasive in real-
world applications, while this problem remains theoretically under-explored. This paper …
world applications, while this problem remains theoretically under-explored. This paper …
PILOT: An -Convergent Approach for Policy Evaluation with Nonlinear Function Approximation
Learning an accurate value function for a given policy is a critical step in solving
reinforcement learning (RL) problems. So far, however, the convergence speed and sample …
reinforcement learning (RL) problems. So far, however, the convergence speed and sample …