Name
Abstract | ||
|---|---|---|
In the field of reinforcement learning there has been recent progress towards safety and high-confidence bounds on policy performance. However, to our knowledge, no practical methods exist for determining high-confidence policy performance bounds in the inverse reinforcement learning setting-where the true reward function is unknown and only samples of expert behavior are given. We propose a sampling method based on Bayesian inverse reinforcement learning that uses demonstrations to determine practical high-confidence upper bounds on the alpha-worst-case difference in expected return between any evaluation policy and the optimal policy under the expert's unknown reward function. We evaluate our proposed bound on both a standard grid navigation task and a simulated driving task and achieve tighter and more accurate bounds than a feature count-based baseline. We also give examples of how our proposed bound can be utilized to perform risk-aware policy selection and risk-aware policy improvement. Because our proposed bound requires several orders of magnitude fewer demonstrations than existing high-confidence bounds, it is the first practical method that allows agents that learn from demonstration to express confidence in the quality of their learned policy. |
Year | Venue | DocType |
|---|---|---|
2018 | national conference on artificial intelligence | Conference |
Volume | Citations | PageRank |
abs/1707.00724 | 2 | 0.38 |
References | Authors | |
18 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel S. Brown | 1 | 34 | 8.67 |
| S. Niekum | 2 | 165 | 23.73 |