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Reinforcement learning allows AI agents to learn by interacting with an environment from "summary" of Artificial Intelligence by Melanie Mitchell
Reinforcement learning is a type of machine learning that enables artificial intelligence agents to learn through trial and error by interacting with an environment. This approach is inspired by the way humans and animals learn through positive and negative feedback received from the environment. The agent takes actions in the environment and receives feedback in the form of rewards or punishments based on the outcomes of those actions. The goal of the agent is to maximize the cumulative reward it receives over time by learning a policy that maps states to actions. The policy is essentially a strategy that the agent uses to decide which action to take in a given state. Through repeated interactions with the environment, the agent learns which actions lead to the highest rewards and adjusts its policy accordingly. One of the key components of reinforcement learning is the exploration-exploitation trade-off. In order to learn the optimal policy, the agent needs to explore different actions to discover which ones lead to the highest rewards. However, it also needs to exploit its current knowledge to maximize its immediate rewards. Striking the right balance between exploration and exploitation is crucial for effective learning. Reinforcement learning algorithms use various techniques to update the agent's policy based on the feedback received from the environment. These techniques include value-based methods, policy-based methods, and actor-critic methods. Value-based methods involve estimating the value of taking a particular action in a given state, while policy-based methods directly learn the policy without estimating the value function.- Reinforcement learning allows AI agents to learn complex behaviors and strategies by interacting with an environment and receiving feedback in the form of rewards or punishments. By iteratively improving their policies based on this feedback, these agents can achieve high levels of performance in a wide range of tasks.
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