Name
Abstract | ||
|---|---|---|
Hierarchical abstraction and curiosity-driven exploration are two common paradigms in current reinforcement learning approaches to break down difficult problems into a sequence of simpler ones and to overcome reward sparsity. However, there is a lack of approaches that combine these paradigms, and it is currently unknown whether curiosity also helps to perform the hierarchical abstraction. As a novelty and scientific contribution, we tackle this issue and develop a method that combines hierarchical reinforcement learning with curiosity. Herein, we extend a contemporary hierarchical actor-critic approach with a forward model to develop a hierarchical notion of curiosity. We demonstrate in several continuous-space environments that curiosity can more than double the learning performance and success rates for most of the investigated benchmarking problems. We also provide our source code (https://github.com/knowledgetechnologyuhh/goal_conditioned_RL_baselines) and a supplementary video (https://www2.informatik.uni-hamburg.de/wtm/videos/chac_icann_roeder_2020.mp4). |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-61616-8_33 | ICANN (2) |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Röder Frank | 1 | 1 | 0.35 |
| Manfred Eppe | 2 | 63 | 11.60 |
| Nguyen Phuong D. H. | 3 | 1 | 0.35 |
| Stefan Wermter | 4 | 1100 | 151.62 |