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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. Mountain car problem - Wikipedia

    en.wikipedia.org/wiki/Mountain_car_problem

    The mountain car problem, although fairly simple, is commonly applied because it requires a reinforcement learning agent to learn on two continuous variables: position and velocity. For any given state (position and velocity) of the car, the agent is given the possibility of driving left, driving right, or not using the engine at all.

  5. 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 ...

  6. Model-free (reinforcement learning) - Wikipedia

    en.wikipedia.org/wiki/Model-free_(reinforcement...

    In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution (and the reward function) associated with the Markov decision process (MDP), [1] which, in RL, represents the problem to be solved. The transition probability distribution (or transition model) and the reward ...

  7. Exploration-exploitation dilemma - Wikipedia

    en.wikipedia.org/wiki/Exploration-exploitation...

    Application in machine learning [ edit ] In the context of machine learning, the exploration-exploitation tradeoff is fundamental in reinforcement learning (RL), a type of machine learning that involves training agents to make decisions based on feedback from the environment.

  8. Savings interest rates today: High-yield accounts still offer ...

    www.aol.com/finance/savings-interest-rates-today...

    Simple interest vs. compound interest. Simple interest refers to the interest you earn on your principal balance only. Let's say you invest $10,000 into an account that pays 3% in simple interest ...

  9. AIXI - Wikipedia

    en.wikipedia.org/wiki/AIXI

    AIXI is a reinforcement learning agent that interacts with some stochastic and unknown but computable environment . The interaction proceeds in time steps, from t = 1 {\displaystyle t=1} to t = m {\displaystyle t=m} , where m ∈ N {\displaystyle m\in \mathbb {N} } is the lifespan of the AIXI agent.