Technical Note: \cal Q-Learning
Machine Learning
Incremental multi-step Q-learning
Machine Learning - Special issue on reinforcement learning
Using background knowledge to speed reinforcement learning in physical agents
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Embedding a Priori Knowledge in Reinforcement Learning
Journal of Intelligent and Robotic Systems
Artificial Intelligence Review
Open Theoretical Questions in Reinforcement Learning
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Using relative novelty to identify useful temporal abstractions in reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Integrating Guidance into Relational Reinforcement Learning
Machine Learning
Opposition-Based Learning: A New Scheme for Machine Intelligence
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
A novel population initialization method for accelerating evolutionary algorithms
Computers & Mathematics with Applications
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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Abstract: Reinforcement learning (RL) is one of the machine intelligence techniques with several characteristics that make it suitable for solving real-world problems. However, RL agents generally face a very large state space in many applications. They must take actions in every state many times to find the optimal policy. In this work, a special type of knowledge about actions is employed to improve the performance of the off-policy, incremental, and model-free reinforcement learning with discrete state and action space. One of the components of RL agent is the action. For each action, its associate opposite action is defined. The actions and opposite actions are implemented in the framework of reinforcement learning to update the value function resulting in a faster convergence. The effects of opposite action on some of the reinforcement learning algorithms are investigated.