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  2. Reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Reinforcement_learning

    Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...

  3. Reinforcement learning from human feedback - Wikipedia

    en.wikipedia.org/wiki/Reinforcement_learning...

    In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning .

  4. Multi-agent reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Multi-agent_reinforcement...

    Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. [ 1 ] Each agent is motivated by its own rewards, and does actions to advance its own interests; in some environments these interests are opposed to the ...

  5. Deep reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Deep_reinforcement_learning

    Various techniques exist to train policies to solve tasks with deep reinforcement learning algorithms, each having their own benefits. At the highest level, there is a distinction between model-based and model-free reinforcement learning, which refers to whether the algorithm attempts to learn a forward model of the environment dynamics.

  6. State–action–reward–state–action - Wikipedia

    en.wikipedia.org/wiki/State–action–reward...

    State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning.It was proposed by Rummery and Niranjan in a technical note [1] with the name "Modified Connectionist Q-Learning" (MCQ-L).

  7. I Serve This 3-Ingredient Drink At Every Holiday Gathering

    www.aol.com/serve-3-ingredient-drink-every...

    How To Make It. Think of it as a merry take on a mimosa: Pour 2 (or 3) parts Prosecco or Champagne to 1 part pomegranate juice in a flute, then plop in a sprig of fresh rosemary for garnish. That ...

  8. Proximal policy optimization - Wikipedia

    en.wikipedia.org/wiki/Proximal_Policy_Optimization

    Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent's decision function to accomplish difficult tasks. PPO was developed by John Schulman in 2017, [1] and had become the default RL algorithm at the US artificial intelligence company OpenAI. [2]

  9. How ex-NFL QB Teddy Bridgewater led HS alma mater to ... - AOL

    www.aol.com/ex-nfl-qb-teddy-bridgewater...

    Teddy Bridgewater's first year as head coach at his alma mater Miami Northwestern ended with winning the Class 3A state championship in Florida.