A parallel network that learns to play backgammon
Artificial Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Practical Issues in Temporal Difference Learning
Machine Learning
Forming coalitions in the face of uncertain rewards
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Temporal difference learning and TD-Gammon
Communications of the ACM
Continual learning in reinforcement environments
Continual learning in reinforcement environments
Coalitions among computationally bounded agents
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Communications of the ACM
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Co-Evolution in the Successful Learning of Backgammon Strategy
Machine Learning
Bidding in reinforcement learning: a paradigm for multi-agent systems
Proceedings of the third annual conference on Autonomous Agents
Learning Team Strategies: Soccer Case Studies
Machine Learning
Multi-agent reinforcement learning: weighting and partitioning
Neural Networks
Computer Go: an AI oriented survey
Artificial Intelligence
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Achieving Efficient and Cognitively Plausible Learning in Backgammon
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
HQ-Learning: Discovering Markovian Subgoals for Non-Markovian Reinforcement Learning
HQ-Learning: Discovering Markovian Subgoals for Non-Markovian Reinforcement Learning
Acquisition of a concession strategy in multi-issue negotiation
Web Intelligence and Agent Systems
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This paper presents a multi-agent reinforcement learning bidding approach (MARLBS) for performing multi-agent reinforcement learning. MARLBS integrates reinforcement learning, bidding and genetic algorithms. The general idea of our multi-agent systems is as follows: There are a number of individual agents in a team, each agent of the team has two modules: Q module and CQ module. Each agent can select actions to be performed at each step, which are done by the Q module. While the CQ module determines at each step whether the agent should continue or relinquish control. Once an agent relinquishes its control, a new agent is selected by bidding algorithms. We applied GA-based MARLBS to the Backgammon game. The experimental results show MARLBS can achieve a superior level of performance in game-playing, outperforming PubEval, while the system uses zero built-in knowledge.