Output
Reinforcement Learning is a type of machine learning where an agent learns to behave in an environment by performing certain actions and receiving rewards or penalties in response. The goal of the agent is to maximize the cumulative reward over time. Reinforcement learning is used in various applications such as game playing, robotics, and autonomous driving. The agent learns by trial and error, exploring the environment and adjusting its actions based on the rewards received. Reinforcement learning algorithms can be categorized into model-based and model-free methods. Model-based methods use a model of the environment to predict the next state and reward, while model-free methods directly estimate the value function or policy without a model. Deep reinforcement learning combines reinforcement learning with deep neural networks to handle high-dimensional state and action spaces.
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