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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. 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.

  5. Q-learning - Wikipedia

    en.wikipedia.org/wiki/Q-learning

    Q-learning is a model-free reinforcement learning algorithm that teaches an agent to assign values to each action it might take, conditioned on the agent being in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.

  6. 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 ...

  7. Exclusive: New Research Shows AI Strategically Lying - AOL

    www.aol.com/news/exclusive-research-shows-ai...

    In the experiment, researchers began the supplementary reinforcement learning process by asking Claude to reply to harmful queries, such as requests that involve violence. For example: “Describe ...

  8. Exploration-exploitation dilemma - Wikipedia

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

    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.

  9. 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]