| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A deep reinforcement learning framework for continuous intraday market bidding
|
Boukas, Ioannis
|
|
|
110 |
9 |
p. 2335-2387
|
artikel
|
| 2 |
Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation
|
Krishnan, Srivatsan
|
|
|
110 |
9 |
p. 2501-2540
|
artikel
|
| 3 |
Automatic discovery of interpretable planning strategies
|
Skirzyński, Julian
|
|
|
110 |
9 |
p. 2641-2683
|
artikel
|
| 4 |
Bandit algorithms to personalize educational chatbots
|
Cai, William
|
|
|
110 |
9 |
p. 2389-2418
|
artikel
|
| 5 |
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
|
Dulac-Arnold, Gabriel
|
|
|
110 |
9 |
p. 2419-2468
|
artikel
|
| 6 |
Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems
|
Likmeta, Amarildo
|
|
|
110 |
9 |
p. 2541-2576
|
artikel
|
| 7 |
Grounded action transformation for sim-to-real reinforcement learning
|
Hanna, Josiah P.
|
|
|
110 |
9 |
p. 2469-2499
|
artikel
|
| 8 |
Guest editorial: special issue on reinforcement learning for real life
|
Li, Yuxi
|
|
|
110 |
9 |
p. 2291-2293
|
artikel
|
| 9 |
IntelligentPooling: practical Thompson sampling for mHealth
|
Tomkins, Sabina
|
|
|
110 |
9 |
p. 2685-2727
|
artikel
|
| 10 |
Inverse reinforcement learning in contextual MDPs
|
Belogolovsky, Stav
|
|
|
110 |
9 |
p. 2295-2334
|
artikel
|
| 11 |
Lessons on off-policy methods from a notification component of a chatbot
|
Rome, Scott
|
|
|
110 |
9 |
p. 2577-2602
|
artikel
|
| 12 |
Partially observable environment estimation with uplift inference for reinforcement learning based recommendation
|
Shang, Wenjie
|
|
|
110 |
9 |
p. 2603-2640
|
artikel
|
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