Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning tetris using the noisy cross-entropy method
Neural Computation
Interactively shaping agents via human reinforcement: the TAMER framework
Proceedings of the fifth international conference on Knowledge capture
Combining manual feedback with subsequent MDP reward signals for reinforcement learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Learning via human feedback in continuous state and action spaces
Applied Intelligence
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As computational learning agents continue to improve their ability to learn sequential decision-making tasks, a central but largely unfulfilled goal is to deploy these agents in real-world domains in which they interact with humans and make decisions that affect our lives. People will want such interactive agents to be able to perform tasks for which the agent's original developers could not prepare it. Thus it will be imperative to develop agents that can learn from natural methods of communication. The teaching technique of shaping is one such method. In this context, we define shaping as training an agent through signals of positive and negative reinforcement. In a shaping scenario, a human trainer observes an agent and reinforces its behavior through push-buttons, spoken word ("yes" or "no"), facial expression, or any other signal that can be converted to a scalar signal of approval or disapproval. We treat shaping as a specific mode of knowledge transfer, distinct from (and probably complementary to) other natural methods of communication, including programming by demonstration and advice-giving. The key challenge before us is to create agents that can be shaped effectively.