Sciweavers

Share
AAAI
1993
9 years 1 months ago
Complexity Analysis of Real-Time Reinforcement Learning
This paper analyzes the complexity of on-line reinforcement learning algorithms, namely asynchronous realtime versions of Q-learning and value-iteration, applied to the problem of...
Sven Koenig, Reid G. Simmons
IJCAI
2003
9 years 1 months ago
Covariant Policy Search
We investigate the problem of non-covariant behavior of policy gradient reinforcement learning algorithms. The policy gradient approach is amenable to analysis by information geom...
J. Andrew Bagnell, Jeff G. Schneider
NIPS
2004
9 years 1 months ago
Brain Inspired Reinforcement Learning
Successful application of reinforcement learning algorithms often involves considerable hand-crafting of the necessary non-linear features to reduce the complexity of the value fu...
François Rivest, Yoshua Bengio, John Kalask...
NIPS
2008
9 years 1 months ago
Regularized Policy Iteration
In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function approximation scheme we propose the use o...
Amir Massoud Farahmand, Mohammad Ghavamzadeh, Csab...
ECML
2003
Springer
9 years 5 months ago
Could Active Perception Aid Navigation of Partially Observable Grid Worlds?
Due to the unavoidable fact that a robot’s sensors will be limited in some manner, it is entirely possible that it can find itself unable to distinguish between differing state...
Paul A. Crook, Gillian Hayes
ATAL
2005
Springer
9 years 5 months ago
Coordinated exploration in multi-agent reinforcement learning: an application to load-balancing
This paper is concerned with how multi-agent reinforcement learning algorithms can practically be applied to real-life problems. Recently, a new coordinated multi-agent exploratio...
Katja Verbeeck, Ann Nowé, Karl Tuyls
CIA
2007
Springer
9 years 6 months ago
Multi-agent Learning Dynamics: A Survey
Abstract. In this paper we compare state-of-the-art multi-agent reinforcement learning algorithms in a wide variety of games. We consider two types of algorithms: value iteration a...
H. Jaap van den Herik, Daniel Hennes, Michael Kais...
ATAL
2007
Springer
9 years 6 months ago
Reducing the complexity of multiagent reinforcement learning
It is known that the complexity of the reinforcement learning algorithms, such as Q-learning, may be exponential in the number of environment’s states. It was shown, however, th...
Andriy Burkov, Brahim Chaib-draa
ATAL
2007
Springer
9 years 6 months ago
Batch reinforcement learning in a complex domain
Temporal difference reinforcement learning algorithms are perfectly suited to autonomous agents because they learn directly from an agent’s experience based on sequential actio...
Shivaram Kalyanakrishnan, Peter Stone
ATAL
2007
Springer
9 years 6 months ago
IFSA: incremental feature-set augmentation for reinforcement learning tasks
Reinforcement learning is a popular and successful framework for many agent-related problems because only limited environmental feedback is necessary for learning. While many algo...
Mazda Ahmadi, Matthew E. Taylor, Peter Stone
books