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Reinforcement learning is AI systems learn by interacting with their environment, aiming for maximum reward.
The reward function is central, guiding agents by assigning values to actions based on their outcomes.
Agents are decision-makers, learning to navigate complex environments through trial and error.
Find a delicate balance: to build RI models exploring new strategies (exploration) and use known strategies (exploitation) for optimal learning.
A policy is a strategy or a rulebook that the agent follows to determine its actions at each state in an environment. Essentially, it's a mapping from perceived states of the environment to actions to be taken when in those states.
From autonomous vehicles to personalized recommendations, Reinforcement Learning is making waves across industries.
Delve into popular algorithms like Q-learning and SARSA that power Reinforcement Learning.
Addressing issues like sparse rewards, high-dimensional spaces, and transfer learning in complex environments.
Understanding the ethical implications of autonomous decision-making and the responsible use of Reinforcement Learning.
Produced by: Analytics Drift Designed by: Prathamesh