TD-Gammon is a neural network that learns to evaluate backgammon positions by playing against itself. It was developed to explore new ideas in reinforcement learning, not to achieve the best backgammon play. The paper discusses the challenges of reinforcement learning, particularly the "temporal credit assignment" problem, and presents TD-Gammon as a method that uses temporal difference (TD) learning to approximate complex nonlinear functions. TD-Gammon's learning process involves self-play, where the neural network evaluates positions and adjusts its weights based on the difference between predicted and actual outcomes. This approach allows the network to learn complex strategies without human supervision. Backgammon is a complex game with high branching factors and probabilistic elements, making it difficult for traditional methods like supervised learning to handle. TD-Gammon's neural network, trained using TD learning, was able to surpass previous programs in backgammon play. It demonstrated strong performance, even without human-designed features, and later improved further with additional features. TD-Gammon's ability to learn from experience and develop its own strategies shows the potential of TD learning in complex domains. The paper compares TD-Gammon with Neurogammon, a previous backgammon program trained using supervised learning. TD-Gammon's results indicate that TD learning can achieve performance comparable to or better than human experts. It also shows that TD-Gammon can learn complex strategies, such as positional judgment, which are difficult to encode manually. The program's self-play training allows it to adapt to new situations and improve over time. TD-Gammon's success highlights the potential of TD learning in reinforcement learning. It demonstrates that TD methods can effectively handle delayed rewards and complex environments. The paper also discusses the implications of TD learning for other domains, including chess and Go, and suggests that further research could expand its applications. Overall, TD-Gammon represents a significant advancement in machine learning, showing that neural networks can learn complex strategies through experience, similar to how humans do.TD-Gammon is a neural network that learns to evaluate backgammon positions by playing against itself. It was developed to explore new ideas in reinforcement learning, not to achieve the best backgammon play. The paper discusses the challenges of reinforcement learning, particularly the "temporal credit assignment" problem, and presents TD-Gammon as a method that uses temporal difference (TD) learning to approximate complex nonlinear functions. TD-Gammon's learning process involves self-play, where the neural network evaluates positions and adjusts its weights based on the difference between predicted and actual outcomes. This approach allows the network to learn complex strategies without human supervision. Backgammon is a complex game with high branching factors and probabilistic elements, making it difficult for traditional methods like supervised learning to handle. TD-Gammon's neural network, trained using TD learning, was able to surpass previous programs in backgammon play. It demonstrated strong performance, even without human-designed features, and later improved further with additional features. TD-Gammon's ability to learn from experience and develop its own strategies shows the potential of TD learning in complex domains. The paper compares TD-Gammon with Neurogammon, a previous backgammon program trained using supervised learning. TD-Gammon's results indicate that TD learning can achieve performance comparable to or better than human experts. It also shows that TD-Gammon can learn complex strategies, such as positional judgment, which are difficult to encode manually. The program's self-play training allows it to adapt to new situations and improve over time. TD-Gammon's success highlights the potential of TD learning in reinforcement learning. It demonstrates that TD methods can effectively handle delayed rewards and complex environments. The paper also discusses the implications of TD learning for other domains, including chess and Go, and suggests that further research could expand its applications. Overall, TD-Gammon represents a significant advancement in machine learning, showing that neural networks can learn complex strategies through experience, similar to how humans do.