Name
Abstract | ||
|---|---|---|
A common problem for deep reinforcement learning networks is a lack of training data to learn specific tasks through generalization. In this paper, we discuss using extended reality to train reinforcement learning agents to overcome this problem. We review popular reinforcement learning and extended reality techniques and then synthesize the information, this allowed us to develop our proposed design for a self learning agent. Meta learning offers an important way forward, but the agents ability to perform self-play is considered crucial for achieving successful Al. Therefore, we focus on improving self-play scenarios for teaching self-learning agents, by providing a supportive environment for improved agent-environment interaction. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.24251/hicss.2019.020 | PROCEEDINGS OF THE 52ND ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES |
Field | DocType | Citations |
Computer science,Knowledge management,Reinforcement learning | Conference | 1 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leonardo Espinosa Leal | 1 | 1 | 1.02 |
| Anthony Chapman | 2 | 1 | 0.68 |
| M. Westerlund | 3 | 43 | 12.89 |