Abstract—When learning how to play a strategy board game, one can measure the relative effectiveness of the learned policies by assessing how often a player wins and how easily these wins are scored. Experimental evidence also shows that when one of the competing players is trained by a sophisticated tutor, performance benefits also flow to the opponent. We present comprehensive experimental evidence that the level of tutor effectiveness is best demonstrated by the improvement of the tutored players opponent; this performance change is termed the pendulum effect. Keywords—board games; reinforcement learning; neural net-works; function approximation; temporal difference learning I
This paper uses experimental data to examine the existence of a teaching strategy among bounded rati...
When learning to play a game well, does it help to play against an opponent who makes the same sort ...
This paper explores the extent to which people learn in repeated games without feedback, and the ext...
A promising approach to learn to play board games is to use reinforcement learning algorithms that c...
A promising approach to learn to play board games is to use reinforcement learning algorithms that c...
A promising approach to learn to play board games is to use reinforcement learning algorithms that c...
Reinforcement learning is applied to computer-based playing of 5x5 Go. We have found that incorporat...
We conduct experiments in which humans repeatedly play one of two games against a computer decision ...
Abstract. We investigate systematically the impact of a minimax tutor in the training of computer pl...
Research in computer game playing has relied primarily on brute force searching approaches rather th...
We report experiments in which humans repeatedly play one of two games against a computer program th...
The authors examine learning in all experiments they could locate involving one hundred periods or m...
We created a computer program that allowed users to play ``No Thanks! , a popular card game that is ...
We present an experimental methodology and results for a machine learning approach to learning openi...
This is a systematic study on learning in the repeated game from the neuroeconomics perspective. The...
This paper uses experimental data to examine the existence of a teaching strategy among bounded rati...
When learning to play a game well, does it help to play against an opponent who makes the same sort ...
This paper explores the extent to which people learn in repeated games without feedback, and the ext...
A promising approach to learn to play board games is to use reinforcement learning algorithms that c...
A promising approach to learn to play board games is to use reinforcement learning algorithms that c...
A promising approach to learn to play board games is to use reinforcement learning algorithms that c...
Reinforcement learning is applied to computer-based playing of 5x5 Go. We have found that incorporat...
We conduct experiments in which humans repeatedly play one of two games against a computer decision ...
Abstract. We investigate systematically the impact of a minimax tutor in the training of computer pl...
Research in computer game playing has relied primarily on brute force searching approaches rather th...
We report experiments in which humans repeatedly play one of two games against a computer program th...
The authors examine learning in all experiments they could locate involving one hundred periods or m...
We created a computer program that allowed users to play ``No Thanks! , a popular card game that is ...
We present an experimental methodology and results for a machine learning approach to learning openi...
This is a systematic study on learning in the repeated game from the neuroeconomics perspective. The...
This paper uses experimental data to examine the existence of a teaching strategy among bounded rati...
When learning to play a game well, does it help to play against an opponent who makes the same sort ...
This paper explores the extent to which people learn in repeated games without feedback, and the ext...