Research map / Reinforcement learning / MDPs and planning
Optimizing Quantiles in Preference-Based Markov Decision Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| optimize the 0.1-quantile instead of expected cumulative reward | Garnet G(100, 5, ceil(log2 100)) · Garnet G(100, 5, ceil(log2 100)), horizon 5 | probability of obtaining a high reward | Helped |
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