no code implementations • 7 Jun 2022 • Tyler Sam, Yudong Chen, Christina Lee Yu
The practicality of reinforcement learning algorithms has been limited due to poor scaling with respect to the problem size, as the sample complexity of learning an $\epsilon$-optimal policy is $\tilde{\Omega}\left(|S||A|H^3 / \epsilon^2\right)$ over worst case instances of an MDP with state space $S$, action space $A$, and horizon $H$.
no code implementations • 23 Jun 2020 • Jake Williams, Abel Tadesse, Tyler Sam, Huey Sun, George D. Montanez
To address this, we prove several novel results related to transfer learning, showing the need to carefully select which sets of information to transfer and the need for dependence between transferred information and target problems.
no code implementations • 3 Jan 2020 • Tyler Sam, Jake Williams, Abel Tadesse, Huey Sun, George Montanez
Previous studies have used a specific success metric within an algorithmic search framework to prove machine learning impossibility results.