Research map / Bandits and online learning / RL theory and MDPs
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use UCLK with the known feature mapping and linear-kernel transition model, without a generative model | Discounted linear kernel MDPs · Linear kernel MDPs (online, T rounds) | regret upper bound: \tilde O(d\sqrt{T}/(1-\gamma)^2) | Helped |
| Construct a hard family of linear kernel MDPs | Any reinforcement learning algorithm · Linear kernel MDPs (online, T rounds) | regret lower bound: \Omega(d\sqrt{T}/(1-\gamma)^{1.5}) | Hurt |
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