Introduction To Q-Learning
Q-Learning is a crucial model-free algorithm in reinforcement learning, focusing on learning the value, or 'Q-value', of actions in specific states. This method excels in environments with unpredictability, as it doesn't need a predefined model of its surroundings. It adapts to stochastic transitions and varied rewards effectively, making it versatile for scenarios where outcomes are uncertain. This flexibility allows Q-Learning to be a powerful tool in scenarios requiring adaptive decision-making without prior knowledge of the environment's dynamics.
Learning Process:
Q-learning works by updating a table of Q-values for each action in each state. It uses the Bellman equation to iteratively update these values based on the observed rewards and its estimation of future rewards. The policy – the strategy of choosing actions – is derived from these Q-values.