Most research in reinforcement learning has focused on stationary environments. In this
paper, we propose several adaptations of Q-learning for a dynamic environment, for both …

In reinforcement learning an autonomous agent learns an optimal policy while interacting
with the environment. In particular, in one-step Q-learning, with each action an agent …

Q-learning is a one of the best model-free reinforcement learning algorithms. The goal is to
find an estimate of the optimal action-value function called Q-value function. The Q-value …

This article proposes and compares different interaction models for reinforcement learning
based on multi-agent system. The cooperation during the learning process is crucial to …

Reinforcement learning refers to a group of methods from artificial intelligence where an
agent performs learning through trial and error. It differs from supervised learning, since …

Q-Learning, which is a well-known model-free reinforcement learning algorithm, a learning
agent explores an environment to update a state-action function. In reinforcement learning …

ABSTRACT Q-learning is a very popular reinforcement learning algorithm being proven to
converge to optimal policies in Markov decision processes. However, Q-learning shows …

Q-learning is an effective model-free reinforcement learning algorithm. However, Q-learning
is centralized and competent only for single agent learning but not multi-agent learning …

Q-Learning is a Reinforcement Learning technique that works by learning an action-value
function that gives the expected utility of taking a given action in a given state and following …

More and more artificial intelligence researchers focused on the reinforcement learning (RL)-
based multi-agent system (MAS). Multi-agent learning problems can in principle be solved …