Clean and flexible implementation of PPO (built on top of stable-baselines3)
-
Updated
Jul 9, 2021 - Python
Clean and flexible implementation of PPO (built on top of stable-baselines3)
Recurrent Policies for Handling Partially Observable Environments
An implementation of Proximal Policy Optimization using TensorFlow. Tested on the OpenAI Gym car racing environment.
Training PPO agents in OpenAI Gym and PyBullet environments.
An autonomous agent that learns to play Atari Bowling using Reinforcement Learning and Proximal Policy Optimization
You can see a reference for Books, Articles, Courses and Educational Materials in this field. Implementation of Reinforcement Learning Algorithms and Environments. Python, OpenAI Gym, Tensorflow.
Apply major Reinforcement Learning algorithms (DQN,PPO,A2C) to CarRacing-v0 from GymAI environment.
Training a PPO to balance a pendulum in a fully observable environment.
Noise-Adaptive Driving Assistance System (NADAS) using Deep Reinforcement Learning, State-Estimation & State Representation
AI agent learns to walk, run, hop and crawl with out any given data using proximal policy optimisation.
Reinforcement learning agent for playing Flappy Bird, as part of a university project
Evaluating the impact of curriculum learning on the training process for an intelligent agent in a video game
Modular Deep RL infrastructure in PyTorch
Repositorio para el contenido relativo al trabajo de fin de máster desarrollado en el Máster de Inteligencia Artificial de la Universidad Internacional de La Rioja (UNIR).
This repository contains my assignment solutions for the Deep Reinforcement Learning course (430.729_003) offered by Seoul National University (Spring 2020).
A custom Gym environment for a Rock-Paper-Scissors game, where a reinforcement learning agent and a CNN model are trained, evaluated, and compared using Ray RLlib and TensorFlow.
Implementation of PPO with TF 2.0 and Pyoneer.
The CAT Optimal Hybrid Solver is a tool designed to tackle the cross array task (CAT) activity designed to assess algorithmic thinking skills in the context of K-12 education.
Training a Reinforcement Learning Agent to Play Flappy Bird.
Add a description, image, and links to the proximal-policy-optimization topic page so that developers can more easily learn about it.
To associate your repository with the proximal-policy-optimization topic, visit your repo's landing page and select "manage topics."