What is a model-free reinforcement learning?

Model-free reinforcement learning is a type of reinforcement learning that does not require a complete understanding or explicit representation of the environment. In model-free reinforcement learning, the algorithm learns directly from experience through trial and error, without building a model of the environment dynamics.

Difference from model-based reinforcement learning

In contrast to model-based reinforcement learning, where the algorithm first learns a model of the environment and then uses this model to make decisions, model-free reinforcement learning directly learns optimal behavior based on the observed rewards and actions, without explicitly modeling the environment.

Utilization in problem-solving

Model-free reinforcement learning is widely used in artificial intelligence and machine learning to solve complex problems such as decision-making and optimization. Algorithms such as Q-learning and SARSA are commonly used in model-free reinforcement learning to train agents to make sequential decisions based on rewards. Deep learning techniques, particularly neural networks, are often employed to approximate the value function or policy in model-free reinforcement learning settings.

In conclusion, model-free reinforcement learning is a powerful approach that can be used to tackle a wide range of complex problems in artificial intelligence and machine learning without the need for explicit modeling of the environment.