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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 ...
Many applications of reinforcement learning do not involve just a single agent, but rather a collection of agents that learn together and co-adapt. These agents may be competitive, as in many games, or cooperative as in many real-world multi-agent systems. Multi-agent reinforcement learning studies the problems introduced in this setting.
Soar [1] is a cognitive architecture, [2] originally created by John Laird, Allen Newell, and Paul Rosenbloom at Carnegie Mellon University.. The goal of the Soar project is to develop the fixed computational building blocks necessary for general intelligent agents – agents that can perform a wide range of tasks and encode, use, and learn all types of knowledge to realize the full range of ...
Q-learning is a model-free reinforcement learning algorithm that teaches an agent to assign values to each action it might take, conditioned on the agent being in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.
Simple reflex agent Learning agent. A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. [1] Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. [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 ...
The Institute for Creative Technologies defines a cognitive architecture as a "hypothesis about the fixed structures that provide a mind, whether in natural or artificial systems, and how they work together — in conjunction with knowledge and skills embodied within the architecture — to yield intelligent behavior in a diversity of complex ...
On graph traversal and sequence-processing tasks with supervised learning, DNCs performed better than alternatives such as long short-term memory or a neural turing machine. [5] With a reinforcement learning approach to a block puzzle problem inspired by SHRDLU, DNC was trained via curriculum learning, and learned to make a plan.