TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Transfer learning in real-time strategy games using hybrid CBR/RL
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Concurrent hierarchical reinforcement learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning potential functions and their representations for multi-task reinforcement learning
Autonomous Agents and Multi-Agent Systems
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Real-Time Strategy (RTS) is a challenging domain for AI, since it involves not only a large state space, but also dynamic actions that agents execute concurrently. This problem cannot be optimally solved through general Q-learning techniques, so we propose a solution using a Semi Markov Decision Process (SMDP). We present a time-based reward shaping technique, TRS, to speed up the learning process in reinforcement learning. Especially, we show that our technique preserves the solution optimality for some SMDP problems. We evaluate the performance of our method in the Spring game Balanced Annihilation, and provide some benchmarks showing the performance of our approach.