Minimax
Minimax is a decision-making algorithm used in adversarial settings where one player tries to maximize their score while the other minimizes it. It is the classical approach for game-playing AI systems.
Understanding Minimax
Minimax is a decision-making algorithm used in adversarial games and competitive scenarios where one player tries to maximize their score while the opponent tries to minimize it. The algorithm recursively explores a game tree, evaluating all possible moves and counter-moves to determine the optimal strategy assuming both players play perfectly. Alpha-beta pruning is a critical optimization that eliminates branches of the tree that cannot influence the final decision, dramatically reducing the search space without changing the result. Minimax was fundamental to early AI game-playing systems like Deep Blue, which defeated world chess champion Garry Kasparov in 1997. In modern AI, minimax principles extend beyond board games to adversarial training, robust optimization, and the generator-discriminator dynamics of generative adversarial networks. The algorithm connects to broader concepts in Markov decision processes and multi-agent systems.
Category
Reinforcement Learning
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Deep Reinforcement Learning
Deep reinforcement learning combines deep neural networks with reinforcement learning algorithms to handle complex, high-dimensional environments. It has achieved superhuman performance in games like Go, chess, and Atari.
Exploration vs Exploitation
Exploration vs exploitation is a fundamental dilemma in reinforcement learning between trying new actions to discover better rewards versus leveraging known good actions. Balancing both is key to optimal long-term performance.
Imitation Learning
Imitation learning is a technique where an AI agent learns to perform tasks by observing and mimicking expert demonstrations. It bridges the gap between supervised learning and reinforcement learning.
Inverse Reinforcement Learning
Inverse reinforcement learning infers the reward function that an expert is optimizing by observing their behavior. It enables AI systems to learn goals and preferences from demonstrations.
Markov Decision Process
A Markov Decision Process (MDP) is a mathematical framework for modeling sequential decision-making problems with probabilistic outcomes. MDPs are the formal foundation for reinforcement learning algorithms.
Policy
A policy in reinforcement learning is a function that maps states to actions, defining the agent's behavior strategy. The goal of RL is to learn an optimal policy that maximizes cumulative reward.
Q-Learning
Q-learning is a model-free reinforcement learning algorithm that learns the value of actions in states to find an optimal policy. It uses a Q-table or neural network to estimate expected cumulative rewards for each state-action pair.
Reinforcement Learning
Reinforcement learning is a machine learning paradigm where an agent learns to make decisions by receiving rewards or penalties for its actions in an environment. It has achieved breakthroughs in game playing, robotics, and AI alignment.