Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Exploiting scale invariant dynamics for efficient information propagation in large teams
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Dynamic potential-based reward shaping
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Efficient opinion sharing in large decentralised teams
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Evaluating POMDP rewards for active perception
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Observer effect from stateful resources in agent sensing
Autonomous Agents and Multi-Agent Systems
Potential-based reward shaping for POMDPs
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Dynamic facts in large team information sharing
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
| Hi-index | 0.00 |
In this research, we focus on active sensing solutions to address challenging properties in complex environments, such as uncertainty, partial observability, non-stationarity, and limited resources. We describe our ongoing contributions, focusing on sensing for both individual agents and cooperating teams. We also outline how we are applying our research to two real-world applications: personal assistants and intelligent survey systems.