A Policy Gradient Learning with CartPole-v0 for Siraj Raval's challenge
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Updated
Dec 13, 2017 - Jupyter Notebook
A Policy Gradient Learning with CartPole-v0 for Siraj Raval's challenge
The idea of B_Pole is that there is a pole standing up on top of a cart. The goal is to balance this pole by wiggling/moving the cart from side to side to keep the pole balanced upright. The environment is deemed successful if we can balance for 200 frames, and failure is deemed when the pole is more than 15 degrees from fully vertical.
OpenAI polecart challenge implemented with DQN
Google DeepMind "Playing Atari with Deep Reinforcement Learning" paper inspired implementation to solve cart pole problem
OpenAI gym CartPole using Keras
A set of scripts that help me learning recent Deep Reinforcement Learning algorithms with Q-learning
solution to cartpole problem of openAI gym with different approaches
Reinforcement Learning algorithms SARSA, Q-Learning, DQN, for Classical and MuJoCo Environments and testing them with OpenAI Gym.
simple and minimal implementation of DQN using target network.
Implementing reinforcement learning algorithms using TensorFlow and Keras in OpenAI Gym
Q-Learning Agent for the CartPole environment from OpenAI Gym
Solutioion to the CartPole problem using Q learning
An implementation of main reinforcement learning algorithms: solo-agent and ensembled versions.
PGuNN - Playing Games using Neural Networks
Solving the custom cartpole balance problem in gazebo environment using Proximal Policy Optimization(PPO)
Collection of policies that use hill climbing variants to solve CartPole
Experimentation with the Cartpole environment in OpenAI's gym
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