An evolving No-Limit Hold’em AI engine for heads-up poker. It begins with rule-based decision bots, generates data for supervised learning models, and advances toward reinforcement learning agents capable of real-time, opponent-aware strategy and optimal decision making.
Opponent exploitation in imperfect game using Neural Networks.
This Rock Paper Scissors AI project combines machine learning and data science techniques to create a highly effective player. It uses an LSTM neural network for move prediction, online learning for real-time adaptation, and pattern recognition. The AI employs opponent modeling, ensemble decision-making, and dynamic learning rates .
Automated Negotiation Agent
Automated Negotiation Agent
Implementation of a Q-Learning agent that utilizes Opponent Modeling Algorithm
BSI-PT algorithm in the paper "Opponent Exploitation Based on Bayesian Strategy Inference and Policy Tracking"
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