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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 ...
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 .
In model-based deep reinforcement learning algorithms, a forward model of the environment dynamics is estimated, usually by supervised learning using a neural network. Then, actions are obtained by using model predictive control using the learned model. Since the true environment dynamics will usually diverge from the learned dynamics, the ...
The forward dynamics model is a function for predicting the next state based on the current state and the current action: : (,) +. The forward dynamics model is trained as the agent plays. The model becomes better at predicting state transition for state-action pairs that had been done many times.
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]
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 ...
Following the success of XCS, LCS were later described as reinforcement learning systems endowed with a generalization capability. [36] Reinforcement learning typically seeks to learn a value function that maps out a complete representation of the state/action space. Similarly, the design of XCS drives it to form an all-inclusive and accurate ...
From the perspective of statistical learning theory, supervised learning is best understood. [4] Supervised learning involves learning from a training set of data. Every point in the training is an input–output pair, where the input maps to an output. The learning problem consists of inferring the function that maps between the input and the ...