# **Reinforcement Learning** Reinforcement Learning (RL) is a machine learning paradigm where agents learn to make decisions by interacting with an environment. The goal is to maximize cumulative rewards over time by exploring and exploiting strategies. # **Key Points** - **Core Components**: - **Agent**: The learner or decision-maker. - **Environment**: The system the agent interacts with. - **Reward Signal**: Feedback indicating success or failure of actions. - **Policy**: Strategy that the agent follows to make decisions. - **Value Function**: Estimates the expected long-term rewards from a state or action. - **Learning Methods**: - **Model-Free RL**: - **Q-Learning**: Updates Q-values for actions based on received rewards. - **Policy Gradient Methods**: Optimize policies directly through gradient descent. - **Model-Based RL**: Uses an internal model of the environment for planning and simulation. - **Applications**: - Robotics for motion planning and control. - Game AI for mastering complex strategies (e.g., AlphaGo). - Autonomous vehicles for navigation and decision-making. - Industrial automation to optimize workflows. # **Insights** Reinforcement learning's trial-and-error mechanism enables agents to autonomously improve decision-making in diverse, complex environments. Incorporating counterfactual reasoning can further enhance its adaptability and efficiency. # **Connections** - Related Notes: [[Counterfactual Reasoning]], [[Reinforcement Learning and Counterfactuals]], [[Neuro Agent Decision-Making Frameworks]] - Broader Topics: [[Machine Learning Frameworks]], [[Adaptive Systems]] # **Questions/Reflections** - How can reinforcement learning be combined with causal inference to improve decision-making? - What are the challenges in scaling RL to real-world, multi-agent environments? # **References** - [[Notes/Counterfactual Analysis]] - [[Generative Models]] - [[Causal Inference Models]]