Applied various Reinforcement Learning (RL) algorithms to determine the optimal policy for diverse Markov Decision Processes (MDPs) specified within the OpenAI Gym library

Mountain Car is a Gym environment. I used this environment to train my model using Q-Learning which is a reinforcement learning technic.

DQN Play Flappy-bird and MountainCar

Deep RL on OpenAI gym environment

Tensorflow based DQN and PyTorch based DDQN Agent for 'MountainCar-v0' openai-gym environment.

This repo constains the implementation of REINFORCE and REINFORCE-Baseline algorithm on Mountain car problem.

MountainCar Deep-Q Network

opengym mountain car continuous model trained with actor critic method

OpenAI MountainCar-v0 DeepRL-based solutions (DQN, DuelingDQN, D3QN)

RL with OpenAI Gym

Solving OpenAI Gym problems.

Solving MountainCar-v0 environment in Keras with Deep Q Learning an Deep Reinforcement Learning algorithm

A solution for the MountainCar-v0 problem of the Gym environment

Deep RL agent for solving MountainCar-v0 environment.

Mountain car problem via Q-learning.

A simple baseline for mountain-car @ gym

MountainCar-v0 is a gym environment. Discretized continuous state space and solved using Q-learning.

PGuNN - Playing Games using Neural Networks

An implementation of main reinforcement learning algorithms: solo-agent and ensembled versions.

Proximal Policy Optimization(PPO) with Intrinsic Curiosity Module(ICM)