Overview
The expected cumulative reward is a fundamental concept in decision theory and reinforcement learning, measuring the anticipated total reward an agent can accumulate over a sequence of actions. It's crucial in problems like Markov decision processes, where the goal is to maximize this cumulative reward. Researchers and engineers use various algorithms, such as Q-learning and SARSA, to compute and optimize the expected cumulative reward. For instance, in a game like chess, the expected cumulative reward could be the probability of winning, with each move contributing to the overall reward. The concept has far-reaching implications, influencing fields from economics to robotics. As of 2023, the study of expected cumulative reward continues to evolve, with applications in complex systems and multi-agent environments. The influence of pioneers like Richard Sutton and Andrew Barto has been significant, with their work on reinforcement learning frameworks shaping the field.
Key Facts
- Year
- 2023
- Origin
- Stanford University
- Category
- Artificial Intelligence
- Type
- Concept