Multi-agent reinforcement learning is a branch of machine learning where multiple agents learn to make decisions and optimize their performance through interactions with each other and their environment. This approach is particularly important in autonomous systems, where coordination and cooperation among various agents, such as vehicles or robots, can enhance overall efficiency and safety. The interactions between agents can lead to complex dynamics, requiring algorithms that account for both individual and collective learning processes.
congrats on reading the definition of multi-agent reinforcement learning. now let's actually learn it.