Solving OpenAI Gym problems.
-
Updated
Jan 12, 2021 - Python
Solving OpenAI Gym problems.
Proximal Policy Optimization(PPO) with Intrinsic Curiosity Module(ICM)
OpenAI MountainCar-v0 DeepRL-based solutions (DQN, DuelingDQN, D3QN)
An implementation of main reinforcement learning algorithms: solo-agent and ensembled versions.
A simple baseline for mountain-car @ gym
Solving MountainCar-v0 environment in Keras with Deep Q Learning an Deep Reinforcement Learning algorithm
RL with OpenAI Gym
MountainCar-v0 is a gym environment. Discretized continuous state space and solved using Q-learning.
Tensorflow based DQN and PyTorch based DDQN Agent for 'MountainCar-v0' openai-gym environment.
Applied various Reinforcement Learning (RL) algorithms to determine the optimal policy for diverse Markov Decision Processes (MDPs) specified within the OpenAI Gym library
PGuNN - Playing Games using Neural Networks
Deep RL on OpenAI gym environment
Deep RL agent for solving MountainCar-v0 environment.
This repo constains the implementation of REINFORCE and REINFORCE-Baseline algorithm on Mountain car problem.
Mountain car problem via Q-learning.
opengym mountain car continuous model trained with actor critic method
MountainCar Deep-Q Network
Add a description, image, and links to the mountaincar-v0 topic page so that developers can more easily learn about it.
To associate your repository with the mountaincar-v0 topic, visit your repo's landing page and select "manage topics."