Reinforcement Learning Algorithms - Explore key reinforcement learning algorithms in machine learning, including Q-learning, Deep Q-Networks, and Policy Gradients. Understand their applications and methodologies.
There is and has been a fruitful flow of concepts and ideas between studies of learning in biological and artificial systems. Much early work that led to the development of reinforcement learning (RL) algorithms for artificial systems was inspired by learning rules first developed in biology by Bu...
Q-learning伪代码 importnumpyasnp# Initialize Q-valuesQ=np.zeros((16,4))# 16 states and 4 actions (up, down, left, right)# Parametersalpha=0.1gamma=0.9epsilon=0.1# Q-learning Algorithmforepisodeinrange(1000):state=0# Starting statewhilestate!=15:# Until reaching the end state# Choose ac...
Reinforcement Learning (source: https://speech.ee.ntu.edu.tw/~hylee/ml/ml2021-course-data/drl_v5.pdf) 强化学习的应用非常广泛,比如: 游戏AI: 训练AI玩各种游戏,例如围棋、Atari游戏等。 机器人控制: 控制机器人完成各种任务,例如行走、抓取物体等。 自动驾驶: 训练汽车在复杂环境中安全行驶。 推荐系统:...
Reinforcement learning is a feedback-based approach where an AI-driven system, or agent, learns how to behave in an environment through repeated iterations.
Discovering Reinforcement Learning Algorithms已经进行了一些尝试,以从与环境分布的交互中学习通用算法(请参见表1进行比较)。EPG[15]使用进化策略来找到策略更新规则。Zheng et al.[39]表明,可以通过奖励函数的形式对用于探索的通用知识进行元学习。ML3[5]使用元梯度对损失函数进行元学习。但是,现有技术仅限于特定领域...
2.3. Classification of reinforcement learning algorithms Broadly there are two main branching points in an RL algorithm – whether the agent has access to (or learns) a model of the environment and the second being what to learn. Based on these two aspects, an RL algorithm will include one(...
Intelligent algorithms can be divided into three main classes: Supervised learning finds a function matching given input–output pairs, and this method requires a training set. Instead, in unsupervised learning, only inputs are given, and the cost function to be minimized depends on the task to ...
Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps.
RL algorithms (Montague, 1999) include Q-Learning (QL), R-Learning, State–Action–Reward–State–Action (SARSA) also, the deep reinforcement learning category has been applied in the Fog computing environment which explained in the next subsection. • QL is a value-based learning algorithm ...