Within the field of machine learning, reinforcement learning refers to the study of how to train agents to complete tasks by updating ("reinforcing") the agents with feedback signals.
Within the field of Machine Learning,machine learning, reinforcement learning refers to the study of how an agent should choose its actions within an environment in order to maximize some kind of reward. Strongly inspiredtrain agents to complete tasks by updating the work developed in behavioral psychology it is essentially a trial and error approach to find the best strategy.agents with feedback signals.
Consider an agent that receives an input informing the agent of the environment's state. Based only on that information, the agent has to make a decision regarding which action to take, from a set, which will influence the state of the environment. This action will in itself change the state of the environment, which will result in a new input, and so on, each time also presenting the agent with the reward (or reinforcement signal) relative to its actions in the environment. The agent's goalIn "policy gradient" approaches, the reinforcement signal is thenoften used to findupdate the ideal strategy whichagent (the "policy"), although sometimes an agent will givedo limited online (model-based) heuristic search to instead optimize the highest reward expectations over time, based on previous experience.signal + heuristic evaluation.
As a reward-maximising AI architecture, RL is distinguished from energy-based architectures such as Active Inference and Joint Embedded Predictive Architectures (JEPA).
RL is distinguished from energy-based architectures such as Active Inference
and, Joint Embedded Predictive Architectures (JEPA), and GFlowNets.