mountaincar-v0
There are 22 repositories under mountaincar-v0 topic.
shivaverma/OpenAIGym
Solving OpenAI Gym problems.
adik993/ppo-pytorch
Proximal Policy Optimization(PPO) with Intrinsic Curiosity Module(ICM)
DanielPalaio/MountainCar-v0_DeepRL
OpenAI MountainCar-v0 DeepRL-based solutions (DQN, DuelingDQN, D3QN)
hjorvardr/RL_Library
An implementation of main reinforcement learning algorithms: solo-agent and ensembled versions.
greatwallet/mountain-car
A simple baseline for mountain-car @ gym
MikeS96/rl_openai
RL with OpenAI Gym
nitish-kalan/MountainCar-v0-Deep-Q-Learning-DQN-Keras
Solving MountainCar-v0 environment in Keras with Deep Q Learning an Deep Reinforcement Learning algorithm
pchandra90/mountainCar-v0
MountainCar-v0 is a gym environment. Discretized continuous state space and solved using Q-learning.
KanishkNavale/DQN-DuelingDQN
Tensorflow based DQN and PyTorch based DDQN Agent for 'MountainCar-v0' openai-gym environment.
xiaohuiduan/flappy-bird-dqn
DQN Play Flappy-bird and MountainCar
aksayushx/Mountain_Car-using-DDQN
Deep RL agent for solving MountainCar-v0 environment.
Anshumaan-Chauhan02/RL-Project
Applied various Reinforcement Learning (RL) algorithms to determine the optimal policy for diverse Markov Decision Processes (MDPs) specified within the OpenAI Gym library
LachubCz/PGuNN
PGuNN - Playing Games using Neural Networks
sebastienbaur/Deep-RL-OpenAI-gym
Deep RL on OpenAI gym environment
John-CYHui/Reinforcement-Learning-REINFORCE
This repo constains the implementation of REINFORCE and REINFORCE-Baseline algorithm on Mountain car problem.
snazau/jbr-rl-MountainCar-v0
Mountain car problem via Q-learning.
FarzamTP/Q-Learning-Mountain-Car
Mountain Car is a Gym environment. I used this environment to train my model using Q-Learning which is a reinforcement learning technic.
JJonahJson/MountainCar-v313
A solution for the MountainCar-v0 problem of the Gym environment
SubaruSpirit/DRL_Training
MountainCar Deep-Q Network
chadrick-kwag/opengym-mountaincar-cont
opengym mountain car continuous model trained with actor critic method