Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the ...
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What is the Q-learning exploration function?
Q-learning can identify an optimal action-selection policy for any given finite Markov decision process, given infinite exploration time and a partly random policy. "Q" refers to the function that the algorithm computes – the expected rewards for an action taken in a given state.
What is the Q-learning theory?
Q-learning is a reinforcement learning algorithm that finds an optimal action-selection policy for any finite Markov decision process (MDP). It helps an agent learn to maximize the total reward over time through repeated interactions with the environment, even when the model of that environment is not known.
What does the Q stand for in Q-learning?
In Q-learning, 'Q' stands for quality. It represents how useful a given action is in achieving future rewards, which is used to create a map system of state and action to maximize expected rewards.
What is exploration vs exploitation in Q-learning?
Exploration is any action that lets the agent discover new features about the environment, while exploitation is capitalizing on knowledge already gained. If the agent continues to exploit only past experiences, it is likely to get stuck in a suboptimal policy.
This paper contributes a new comparison of exploration strategies in a stochastic maze problem. We use Q-learning [6] as reinforcement learning algorithm and.
This paper evaluates four different exploration strategies combined with Q-learning using random stochastic mazes to investigate their performances. We will ...
Nov 25, 2012 · To overcome this, we're going to need to introduce exploration. The standard way to get exploration is to introduce an additional term, epsilon ...
A set of Deep Reinforcement Learning Agents implemented in Tensorflow. - DeepRL-Agents/Q-Exploration.ipynb at master · awjuliani/DeepRL-Agents.
Apr 16, 2024 · A major challenge in reinforcement learning for continuous state-action spaces is exploration, especially when reward landscapes are very ...
May 5, 2021 · Any exploration function that ensures the behaviour policy covers all possible actions will work in theory with Q learning.
Mar 24, 2023 · In this tutorial, we'll learn about epsilon-greedy Q-learning, a well-known reinforcement learning algorithm. We'll also mention some basic ...
In this work, we applied DQN algorithm with different exploration-exploitation strategies to solve traditional route-finding problems. The experimental results ...