cartpole-environment

There are 0 repository under cartpole-environment topic.

BY571 / Upside-Down-Reinforcement-Learning
Upside-Down Reinforcement Learning (⅂ꓤ) implementation in PyTorch. Based on the paper published by Jürgen Schmidhuber.
cartpole-environment continuous-action-space discrete-action-space machine-learning machine-learning-algorithms python pytorch reinforcement-learning reinforcement-learning-algorithms upside-down upside-down-reinforcement-learning
Language:Jupyter Notebook 77
imraviagrawal / Reinforcement-Learning-Implementation
Implementation of Reinforcement Algorithms from scratch
reinforcement-learning cartpole cross-entropy cross-entropy-policy-search td-learning gridworld gridworld-environment blackbox-optimization sarsa q-learning cartpole-environment reinforcement-algorithms sarsa-lambda q-learning-lambda actor-critic-algorithm mountain-car reinforce
Language:Python 9
TTitcombe / CartPoleSwingUp
Custom environment for OpenAI gym
openai-gym openai-gym-environment gym-environment cartpole reinforcement-learning-environments openai-gym-environments gym-environments gym cartpole-environment cartpole-v1
Language:Python 3
chaddy1004 / beating-the-dead-cartpole
My attempt to beat dqn to the death by implementing as many RL algorithms on it possible
cartpole-environment gym-environment reinforcement-learning reinforcement-learning-algorithms
Language:Python 2
jangThang / Cartpole
In Cartpole Reinforcement Learning Environment, DQN, DDQN, and Dueling DQN methods are trained respectively.
reinforcement-learning openai-gym-environments cartpole-environment dqn ddqn dueling-dqn
Language:Jupyter Notebook 2
jaysonph / TD-A2C
Advantage Actor Critic with Temporal Difference on Cartpole-v0
reinforcement-learning deep-learning deep-reinforcement-learning pytorch cartpole-environment
Language:Python 2
MZayed47 / A3C_Agent
The Asynchronous Advantage Actor Critic (A3C) algorithm is one of the newest algorithms to be developed under the field of Deep Reinforcement Learning Algorithms. This algorithm was developed by Google’s DeepMind which is the Artificial Intelligence division of Google. In this repository, I have my implementations of A3C on Cartpole game, Robot arm, etc.
a3c a3c-agent asynchronous-advantage-actor-critic cartpole-environment reinforcement-learning reinforcement-learning-agent
Language:Python 2
rusty-sj / Reinforcement-Learning-Algorithms-with-Ray-Framework-and-Intel-DevCloud
Implementation of certain crucial algorithms in the field of reinforcement learning.
ray-framework devcloud reinforcement-learning distributed-reinforcement-learning q-learning sarsa-algorithm value-iteration-algorithm policy-iteration deep-reinforcement-learning atari dqn cartpole-environment frozenlake
Language:Jupyter Notebook 2
lcao300 / cartpole
a q-learning implementation of the cart pole environment in openai gym
open-ai-gym open-ai cartpole-v0 cartpole cartpole-environment reinforcement-learning reinforcement-learning-agent q-learning
Language:Python 1
Anca-Mt / CartPole-DeepQLearning
DQN agent with e-greedy / softmax policy, experience replay and target network.
cartpole-environment dqn policy reinfrocement-learning softmax e-greedy open-ai-gym
Language:Python 0
kth0522 / DQN
dqn-tensorflow cartpole-v1 cartpole-environment tensorflow2
Language:Python 0
skal-chin / Cart-Pole
control cartpole-environment
Language:Python 0
sprasai94 / Cart-Pole-using-Reinforcement-Learning-DQN---Deep-Q-Learning-
cartpole-environment machine-learning-algorithms python reinforcement-learning-algorithms
Language:Python 0
Twinkletanna / Reinforcement_Learning
Implementation of RL algorithms in various environments
cartpole-environment gridworld-environment mountaincar reinforcement-learning
Language:Python 0
JayLohokare / gradient-ascent-stochastic-policy-learning
Open AI Cartpole environment gradient ascent
cartpole-environment cross-entropy-method deep-reinforcement-learning gradient-ascent
Language:Jupyter Notebook
roshray / AI_On_Balancing_Pole
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.
cartpole-environment cartpole-v0
Language:Python
sayanmondal31 / cartpole-environment
cartpole environment
cartpole-environment cartpole
Language:Python