Name
Abstract | ||
|---|---|---|
As reinforcement learning agents are tasked with solving more challenging and diverse tasks, the ability to incorporate prior knowledge into the learning system and to exploit reusable structure in solution space is likely to become increasingly important. The KL-regularized expected reward objective constitutes one possible tool to this end. It introduces an additional component, a default or prior behavior, which can be learned alongside the policy and as such partially transforms the reinforcement learning problem into one of behavior modelling. In this work we consider the implications of this framework in cases where both the policy and default behavior are augmented with latent variables. We discuss how the resulting hierarchical structures can be used to implement different inductive biases and how their modularity can benefit transfer. Empirically we find that they can lead to faster learning and transfer on a range of continuous control tasks. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1903.07438 | 2 | 0.36 |
References | Authors | |
35 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dhruva Tirumala | 1 | 13 | 2.16 |
| Hyeonwoo Noh | 2 | 699 | 25.15 |
| Alexandre Galashov | 3 | 9 | 3.82 |
| Leonard Hasenclever | 4 | 20 | 5.42 |
| Arun Ahuja | 5 | 72 | 7.45 |
| Greg Wayne | 6 | 592 | 31.86 |
| Razvan Pascanu | 7 | 2596 | 199.21 |
| Yee Whye Teh | 8 | 6253 | 539.26 |
| Nicolas Heess | 9 | 1762 | 94.77 |