|Title||Object-Focused Advice in Reinforcement Learning|
|Publication Type||Conference Paper|
|Year of Publication||2016|
|Authors||Krening, S., B. Harrison, K. M. Feigh, C. Isbell, and A. Thomaz|
|Conference Name||Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems|
In order for robots and intelligent agents to interact with and learn from people with no machine-learning expertise, robots should be able to learn from natural human instruction. Many human explanations consist of simple sentences without state information, yet most machine learning techniques that incorporate human guidance cannot use non-specific explanations. This work aims to learn policies from a few sentences that aren't state specific. The proposed Object-focused advice links an object to an action, and allows a person to generalize over an object's state space. To evaluate this technique, agents were trained using Object-focused advice collected from participants in an experiment in the Mario Bros. domain. The results show that Object-focused advice performs better than when no advice is given, the agent can learn where to apply the advice in the state space, and the agent can recover from adversarial advice. Also, including warnings of what not do to in addition to advice of what actions to take improves performance.
Object-Focused Advice in Reinforcement Learning
Submitted by Samantha Krening on Tue, 2016-06-07 10:53