Research map / Reinforcement learning / MDPs and planning
Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Adversarial integral equation with smoothed (RKHS-projected) discriminator plus optimism-based exploration (OP-TENET) | linear-structured POMDPs with infinite observation and state spaces · theoretical sample complexity | suboptimality of averaged policies after K iterations | No clear effect |
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