Reinforcement learning (RL) is defined as a sub-field of machine learning that enables AI-based systems to take actions in a dynamic environment through trial and error methods to maximize the collective rewards based on the feedback generated for respective actions
Reinforcement Learning (RL) is an area of machine learning that focuses on how an intelligent agent should take actions in a dynamic environment to maximize the cumulative reward. It is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
Unlike supervised learning, RL does not require labelled input/output pairs to be presented, and it does not need sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).
The environment in RL is typically stated in the form of a Markov decision process (MDP), and many RL algorithms for this context use dynamic programming techniques. The main difference between classical dynamic programming methods and RL algorithms is that the latter do not assume knowledge of an exact mathematical model of the MDP.
In essence, RL is about learning the optimal behavior in an environment to obtain maximum reward. It uses algorithms that learn from outcomes and decide which action to take next. After each action, the algorithm receives feedback that helps it determine whether the choice it made was correct, neutral, or incorrect. It is a good technique to use for automated systems that have to make a lot of small decisions without human guidance.
Here's a simple example: Imagine a robot trying to navigate a maze to find a reward. The robot learns by trying all possible paths and then choosing the path which gives it the reward with the least hurdles. Each right step will give the robot a reward and each wrong step will subtract the reward. The total reward will be calculated when it reaches the final reward..
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Reinforcement learning (RL) is defined as a sub-field of machine learning that enables AI-based systems to take actions in a dynamic environment through trial and error methods to maximize the collective rewards based on the feedback generated for respective actions
Reinforcement Learning (RL) is an area of machine learning that focuses on how an intelligent agent should take actions in a dynamic environment to maximize the cumulative reward. It is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
Unlike supervised learning, RL does not require labelled input/output pairs to be presented, and it does not need sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).
The environment in RL is typically stated in the form of a Markov decision process (MDP), and many RL algorithms for this context use dynamic programming techniques. The main difference between classical dynamic programming methods and RL algorithms is that the latter do not assume knowledge of an exact mathematical model of the MDP.
In essence, RL is about learning the optimal behavior in an environment to obtain maximum reward. It uses algorithms that learn from outcomes and decide which action to take next. After each action, the algorithm receives feedback that helps it determine whether the choice it made was correct, neutral, or incorrect. It is a good technique to use for automated systems that have to make a lot of small decisions without human guidance.
Here's a simple example: Imagine a robot trying to navigate a maze to find a reward. The robot learns by trying all possible paths and then choosing the path which gives it the reward with the least hurdles. Each right step will give the robot a reward and each wrong step will subtract the reward. The total reward will be calculated when it reaches the final reward..
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