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Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation
To the best of our knowledge, our algorithm is the first efficient rate optimal regret minimization algorithm for adversarial CMDPs that operates under the minimal standard assumption of online function approximation.
Eluder-based Regret for Stochastic Contextual MDPs
To the best of our knowledge, our algorithm is the first efficient and rate-optimal regret minimization algorithm for CMDPs that operates under the general offline function approximation setting.
Optimism in Face of a Context: Regret Guarantees for Stochastic Contextual MDP
For the latter, our algorithm obtains regret bound of $\widetilde{O}( (H+{1}/{p_{min}})H|S|^{3/2}\sqrt{|A|T\log(\max\{|\mathcal{G}|,|\mathcal{P}|\}/\delta)})$ with probability $1-\delta$, where $\mathcal{P}$ and $\mathcal{G}$ are finite and realizable function classes used to approximate the dynamics and rewards respectively, $p_{min}$ is the minimum reachability parameter, $S$ is the set of states, $A$ the set of actions, $H$ the horizon, and $T$ the number of episodes.
Learning Efficiently Function Approximation for Contextual MDP
We study learning contextual MDPs using a function approximation for both the rewards and the dynamics.
