Creating advice-taking reinforcement learners
Machine Learning - Special issue on reinforcement learning
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The MAXQ Method for Hierarchical Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Least-squares policy iteration
The Journal of Machine Learning Research
Behavior transfer for value-function-based reinforcement learning
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Using Homomorphisms to transfer options across continuous reinforcement learning domains
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Using advice to transfer knowledge acquired in one reinforcement learning task to another
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Traditionally, research in the reinforcement learning (RL) community has been devoted to developing domain-independent algorithms such as SARSA [13], Q-learning [16], prioritized sweeping [8], or LSPI [6], that are designed to work for any given state space and action space. However, the modus operandi in RL research has been for a human expert to re-code each learning environment, including defining the actions and state features, as well as specifying the algorithm to be used. Typically each new RL experiment is run by explicitly calling a new program (even when learning can be biased by previous learning experiences, as in transfer learning [10, 15, 14]). Thus, while standards have developed for describing and testing individual RL algorithms (e.g., RL-Glue [17]), no such standards have developed for the problem of describing complete tasks to a preexisting agent.