#### Deep Q-Network Solves Cart and Pole – Reinforcement Learning Code Project

- Frank
- March 28, 2022
- agent environment
- AI
- AlphaGo
- artificial intelligence
- artificial neural network
- Bellman equation
- CNN
- Deep Learning
- Deep Q-network
- DQN
- Education
- experience replay
- Machine Learning
- markov decision process
- MDP
- Neural Network
- OpenAI Five
- OpenAI Gym
- policy gradients
- policy network
- Python
- PyTorch
- Q-learning
- Q-value
- Reinforcement Learning
- replay memory
- SGD
- stochastic gradient descent
- Supervised Learning
- TensorFlow
- Tutorial
- Unsupervised Learning

In this episode, learn how to use a deep Q-network to solve the Cart and Pole environment.

Read More#### Dueling Deep Q Learning with Tensorflow 2 & Keras

- Frank
- April 1, 2020
- deep q learning
- deep q learning algorithm
- deep q learning keras
- deep q learning lunar lander
- deep q learning networks
- deep q learning tutorial
- deep q network
- Deep Reinforcement Learning
- dueling deep q learning agent
- dueling deep q learning algorithm
- dueling deep q learning keras
- dueling deep q learning lunar lander
- dueling deep q learning openai gym
- dueling deep q learning tensorflow
- dueling deep q learning tutorial
- OpenAI Gym
- reinforcement learning python

Machine Learning with Phil dives into Deep Q Learning with Tensorflow 2 and Keras. Dueling Deep Q Learning is easier than ever with Tensorflow 2 and Keras. In this tutorial for deep reinforcement learning beginners we’ll code up the dueling deep q network and agent from scratch, with no prior experience needed. We’ll train an […]

Read More#### Hands-On Guide to OpenAI Gym Custom Environments

OpenAI Gym is a well known RL environment/community for developing and comparing Reinforcement Learning agents. OpenAI Gym doesn’t make assumptions about the structure of the agent and works out well with any numerical computation library such as TensorFlow, PyTorch. The gym also provides various types of environments. In this hands-on guide, learn how to develop […]

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