Sequential Optimal Experimental Design on Location Finding (LF) (test)
4.79sPCERandom
Evaluation Results
| Method | Links | |
|---|---|---|
| RandomEvaluation Horizon (T)=10, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=5*10^52025.12 | 4.79 | |
| RandomEvaluation Horizon (T)=30, Number of Dimensions (K)=1, Number of Contrastive Samples (L)=10^62025.12 | 5.17 | |
| SG-BOEDEvaluation Horizon (T)=30, Number of Dimensions (K)=1, Number of Contrastive Samples (L)=10^62025.12 | 5.25 | |
| SG-BOEDEvaluation Horizon (T)=10, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=5*10^52025.12 | 5.55 | |
| JADAITraining Horizon (u_t)=u10, Evaluation Horizon (T)=10, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=5*10^52025.12 | 6.47 | |
| JADAITraining Horizon (u_t)=u20, Evaluation Horizon (T)=10, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=5*10^52025.12 | 6.71 | |
| JADAITraining Horizon (u_t)=u30, Evaluation Horizon (T)=10, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=5*10^52025.12 | 6.74 | |
| RandomEvaluation Horizon (T)=20, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=5*10^52025.12 | 7 | |
| DADEvaluation Horizon (T)=30, Number of Dimensions (K)=1, Number of Contrastive Samples (L)=10^62025.12 | 7.33 | |
| SG-BOEDEvaluation Horizon (T)=20, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=5*10^52025.12 | 7.7 | |
| RL-BOEDEvaluation Horizon (T)=30, Number of Dimensions (K)=1, Number of Contrastive Samples (L)=10^62025.12 | 7.7 | |
| iDADEvaluation Horizon (T)=10, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=5*10^52025.12 | 7.75 | |
| DADEvaluation Horizon (T)=10, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=5*10^52025.12 | 7.97 | |
| RandomEvaluation Horizon (T)=30, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=10^62025.12 | 8.3 | |
| SG-BOEDEvaluation Horizon (T)=30, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=10^62025.12 | 8.84 | |
| ALINEEvaluation Horizon (T)=30, Number of Dimensions (K)=1, Number of Contrastive Samples (L)=10^62025.12 | 8.91 | |
| JADAITraining Horizon (u_t)=u30, Evaluation Horizon (T)=30, Number of Dimensions (K)=1, Number of Contrastive Samples (L)=10^62025.12 | 9.62 | |
| iDADEvaluation Horizon (T)=20, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=5*10^52025.12 | 10.08 | |
| DADEvaluation Horizon (T)=20, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=5*10^52025.12 | 10.42 | |
| JADAITraining Horizon (u_t)=u20, Evaluation Horizon (T)=20, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=5*10^52025.12 | 10.48 | |
| JADAITraining Horizon (u_t)=u30, Evaluation Horizon (T)=20, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=5*10^52025.12 | 10.9 | |
| DADEvaluation Horizon (T)=30, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=10^62025.12 | 10.97 | |
| RL-BOEDEvaluation Horizon (T)=30, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=10^62025.12 | 11.73 | |
| RL-sCEEEvaluation Horizon (T)=30, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=10^62025.12 | 12.31 | |
| JADAITraining Horizon (u_t)=u30, Evaluation Horizon (T)=30, Number of Dimensions (K)=2, Number of Contrastive Samples (L)=10^62025.12 | 12.82 |