Abstract
Reward shaping has been shown to significantly improve an agent's performance in reinforcement learning. Plan-based reward shaping is a successful approach in which a STRIPS plan is used in order to guide the agent to the optimal behaviour. However, if the provided domain knowledge is wrong, it has been shown the agent will take longer to learn the optimal policy. Previously, in some cases, it was better to ignore all prior knowledge despite it only being partially erroneous. This paper introduces a novel use of knowledge revision to overcome erroneous domain knowledge when provided to an agent receiving plan-based reward shaping. Empirical results show that an agent using this method can outperform the previous agent receiving plan-based reward shaping without knowledge revision.
| Original language | English |
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| Title of host publication | 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 |
| Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
| Pages | 1245-1246 |
| Number of pages | 2 |
| Volume | 2 |
| Publication status | Published - 2013 |
| Event | 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 - Saint Paul, MN, United States Duration: 6 May 2013 → 10 May 2013 |
Conference
| Conference | 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 |
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| Country/Territory | United States |
| City | Saint Paul, MN |
| Period | 6/05/13 → 10/05/13 |
Keywords
- Knowledge Revision
- Reinforcement Learning
- Reward Shaping