Reinforcement Learning (RL)

Reinforcement Learning Latest News

In a paper published recently, the DeepSeek-AI team reported that their model, called just R1, could develop new forms of reasoning using reinforcement learning, a method of trial and error guided only by rewards for correct answers.

About Reinforcement Learning 

• It is defined as a sub-field of machine learning (ML) 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. 
• In RL, an autonomous agent learns to perform a task by trial and error in the absence of any guidance from a human user.
• RL algorithms use a reward-and-punishment paradigm as they process data.
• RL is based on the hypothesis that all goals can be described by the maximization of expected cumulative reward. 
• The RL agent learns about a problem by interacting with its environment. The environment provides information on its current state. 
• The agent then uses that information to determine which actions(s) to take. 
• If that action obtains a reward signal from the surrounding environment, the agent is encouraged to take that action again when in a similar future state. 
• This process repeats for every new state thereafter. 
• Over time, the agent learns from rewards and punishments to take actions within the environment that meet a specified goal.
• The learning process in RL is driven by a feedback loop that consists of four key elements:
  • Agent: The learner and decision-maker in the system.
  • Environment: The external world the agent interacts with.
  • Actions: The choices the agent can make at each step.
  • Rewards: The feedback the agent receives after taking an action, indicating the desirability of the outcome.
• It particularly addresses sequential decision-making problems in uncertain environments and shows promise in artificial intelligence development.

Source: TH

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