This repository displays the use of Reinforcement Learning, specifically QLearning, REINFORCE, and Actor Critic (A2C) methods to play CartPole-v0 of OpenAI Gym.
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Updated
Jan 14, 2021 - Python
This repository displays the use of Reinforcement Learning, specifically QLearning, REINFORCE, and Actor Critic (A2C) methods to play CartPole-v0 of OpenAI Gym.
Implementations of different RL algorithms.
Collaboration and competition project of Udacity Deep Reinforcement Learning Nanodegree
Reinforcement learning agents implemented with PyTorch
Solving the gym Pendulum-v0 environment using Policy Gradient method
A Python-based repository with implementations of RL algorithms, featuring visualization tools and benchmarks
Several RL-agents are tested on classical environments and benchmarked against their stable-baselines implementation.
Compare efficiency and effectiveness between simple Dense layer and LSTM layer using CartPole
Population Based Training of neural networks for multiagent environments
reinforcement learning
This repository showcases the implementation of a PPO Clip first-order method to solve the LunarLander discrete environment
Actor critic methods explored using Pytorch and OpenAI Gym on CartPole and Acrobot environments..
Implementing some RL algorithms (using PyTorch) on the CartPole environment by OpenAI.
opengym mountain car continuous model trained with actor critic method
Reinforcement Learning: Policy Gradient Methods
A Reinforcement Learning Playground
🦾 Utilizing a Deep Deterministic Policy Gradient algorithm to train robotic simulations in continuous action space
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