How Modern Game Theory is Influencing Multi-Agent Reinforcement Learning Systems
Game theory dynamics are present everywhere in multi-agent reinforcement learning systems. What do you need to know about it?
Most artificial intelligence(AI) systems nowadays are based on a single agent tackling a task or, in the case of adversarial models, a couple of agents that compete against each other to improve the overall behavior of a system. However, many cognition problems in the real world are the result of knowledge built by large groups of people. Take for example a self-driving car scenario, the decisions of any agent are the result of the behavior of many other agents in the scenario. Many scenarios in financial markets or economics are also the result of coordinated actions between large groups of entities. How can we mimic that behavior in artificial intelligence(AI) agents?
Multi-Agent Reinforcement Learning(MARL) is the deep learning discipline that focuses on models that include multiple agents that learn by dynamically interacting with their environment. While in single-agent reinforcement learning scenarios the state of the environment changes solely as a result of the actions of an agent, in MARL scenarios the environment is subjected to the actions of all agents. From that…
