Q-Learning is a model-free, off-policy Reinforcement learning approach that determines the optimal course of action when presented with a given environment. This action is selected randomly and is based on the expectation of maximizing reward. Q-Learning does not require a pre-defined policy and can instead generate its own as it explores the environment. This enables the agent to take dynamic actions while operating outside of a given policy. Ultimately, this allows for efficient decision-making in any given context.
What are the Uses of Q-Learning?
Q-learning
- Helps train agents to make optimal decisions based on the current state of the environment to maximize rewards and minimize losses.
- Is used in the field of natural language processing to train chatbots and virtual assistants to ensure optimal responses based on the user’s query.
- Enables robots to learn optimal control policies for various tasks.
- Trains autonomous vehicles to make optimal decisions based on the current state of the environment to maximize safety and efficiency.
