Reinforcement Learning (RL) is a paradigm in artificial intelligence that deals with the interaction of an agent with an environment to learn optimal decision-making strategies. Unlike supervised learning, where the model is trained on labeled data, and unsupervised learning, where the model discovers patterns in unlabeled data, reinforcement learning focuses on training agents to make sequential decisions through trial and error. The agent learns by receiving feedback from the environment in the form of rewards or penalties based on its actions.
The essence of reinforcement learning lies in the concept of learning through interaction. The agent explores its environment, takes actions, observes the outcomes, and adapts its decision-making processes to maximize cumulative rewards over time. A fundamental element of RL is the trade-off between exploration and exploitation, where the agent must balance between trying new actions to discover better strategies and exploiting its current knowledge to achieve higher rewards.
Reinforcement learning has found applications in diverse domains, from robotics and game playing to autonomous driving and recommendation systems. Algorithms like Q-learning, Deep Q Networks (DQN), and Proximal Policy Optimization (PPO) have been developed to solve RL problems. The field has witnessed significant advancements, especially with the use of deep learning techniques that enable agents to learn complex and high-dimensional tasks.
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