Description
Session chair: Tejas Bodas
The robust $\phi$-regularized Markov Decision Process (RRMDP) framework focuses on designing control policies that are robust against parameter uncertainties due to mismatches between the simulator (nominal) model and real-world settings. This work makes \emph{two} important contributions. First, we propose a \textit{model-free} algorithm called \textit{Robust $\phi$-regularized fitted...
In this talk, we introduce a policy-gradient method for model-based Reinfocement Learning (RL) that exploits a type of stationary distribution commonly obtained from Markov Decision Processes (MDPs) in stochastic networks, queueing systems and statistical mechanics.
Specifically, when the stationary distribution of the MDP belongs to an exponential family that is parametrized by policy...
In the realm of multi-arm bandit problems, the Gittins index policy is known to be optimal in maximizing the expected total discounted reward obtained from pulling the Markovian arms. In most realistic scenarios however, the Markovian state transition probabilities are unknown and therefore the Gittins indices cannot be computed. One can then resort to reinforcement learning (RL) algorithms...
