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.

DQN Play Flappy-bird and MountainCar

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

Application of Reinforcement Learning algorithms (DQN,DRQN,PPO,A2C) to gym's MountainCar-v0

Deep RL agent for solving MountainCar-v0 environment.

Deep RL on 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

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

Mountain car problem via Q-learning.

Reinforcement learning solution for the Mountain Car problem using value iteration, policy iteration, and Q-learning in OpenAI Gym.

Project of COMP4125 in 2024-2025.