The main objective of reinforcement learning (RL) is to enable an agent to act optimally to maximize the cumulative long-term reward. Q-learning is a model free RL algorithm, which iteratively learns a long-term reward function "Q" given the current state and action. The Deep Q-Learning Ne...
Lunar Lander is one of the environments in Open AI's Gym library. Simply put, an environment represents a problem or task to be solved. In this case, we will try to solve the environment using Deep Q-Learning Algorithm with Experience Replay....
However, traditional Q-learning has its challenges. It struggles with scalability as the state space grows and is less effective in environments with continuous state and action spaces. This is where Deep Q Networks (DQNs) come in. DQNs use neural networks to approximate the Q-values, enabling...
Building a reinforcement learning agent in Keras CartPole CartPole neural network architecture Memory Policy Agent Training Results Lunar Lander Lunar Lander network architecture Memory and policy Agent Training Results Summary Generative Adversarial Networks An overview of the GAN Deep Convolutional GAN archite...
当然!以下是一个将 `ReinforcementLearning. jl` 和 `Flux. jl` 无缝集成的代码示例,用于解决经典的 Lunar Lander 环境: ```julia using ReinforcementLearning, Flux, JuliaRL # 定义深度 Q 网络模型 struct DQNPolicy <: AbstractPolicy model::Chain ...
Programming Assignment Deep Q-Learning - Lunar Lander Certificate of Completion Specialization Certificate Course Review : This Course is a best place towards becoming a Machine Learning Engineer. Even if you're an expert, many algorithms are covered in depth such as decision trees which may help...
Lunar Lander requires an agent to learn to process 8-dimensional continuous sensor input and in return produce a two-dimensional continuous control output (i.e. action) ranging from \(-1.0\) to 1.0. The aim of this problem is to smoothly and accurately guide the lander robot to land on ...
D2D-SQL uses the data to initialise a neural network which is then allowed to continue learning using another RL method. We demonstrate the viability of our algorithms with Cartpole, Lunar Lander and an aircraft manoeuvring problem, three continuous-space environments with low-dimensional state ...
Programming Assignment Deep Q-Learning - Lunar Lander Certificate of Completion Specialization Certificate Course Review : This Course is a best place towards becoming a Machine Learning Engineer. Even if you're an expert, many algorithms are covered in depth such as decision trees which may help...
Tan et al. [93]Adversarial TrainingPPOLunar LanderAction space Attacks Lee et al. [54]Adversarial TrainingPPOpoint goal, car goal [84]Action space Attacks Vinitsky et al. [99]Adversarial Training using PopulationsPPOHopper, Ant, Half-cheetahGeneric Adversarial Attacks ...