Clean and flexible implementation of PPO (built on top of stable-baselines3)
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Updated
Jul 9, 2021 - Python
Clean and flexible implementation of PPO (built on top of stable-baselines3)
This repository contains my assignment solutions for the Deep Reinforcement Learning course (430.729_003) offered by Seoul National University (Spring 2020).
Cognitive Generative Intelligent Task Offloading for Digital Twins of Vehicular Networks This repository contains the code and resources for the implementation of cognitive generative intelligent task offloading in digital twins for vehicular networks.
A pytorch project to easily run experiments on OpenAI's Procgen Benchmark
My lab work of “Generative AI with Large Language Models” course offered by DeepLearning.AI and Amazon Web Services on coursera.
Built and trained a model using OpenAI gym, NES emulator to play Super Mario. Optimized the model using preprocessing techniques and vectorization. The algorithm used is PPO (Proximal Policy Optimal) along with Reinforcement Learning.
A demonstration of some prominent reinforcement learning algorithms
Single file implementation of Deep Reinforcement Learning algorithm (PPO) based on LunarLander-v2 environment
PPO implementation for the cable suspended load quadrotor
Train double-jointed arms to reach target locations using Proximal Policy Optimization (PPO) in Pytorch
This project implements an agent for playing the SonicTheHedgehog2 game from a ROM file using the Proximal Policy Optimization (PPO) algorithm from the stablebaselines3 library. The agent is trained to learn the optimal actions to take at each step in the game in order to complete the level and maximize the score.
This repository provides an implementation of Othello game playing agents trained using reinforcement learning techniques.
MarioPPO implementation uses the TensorFlow machine learning platform
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Example PPO implementation with ReLAx
Ask About Symptoms is an LLM that has an in-depth understanding of health. The creator of the original version known as DoctorGPT, Siraj Raval, says it works offline, it's cross-platform, & the health data is said to be kept private. We are learning how to build this in our community.
SimplyPPO replicates Proximal-Policy-Optimization with minimum (~250) lines of code in clean, readable PyTorch style, while trying to use as few additional tricks and hyper-parameters as possible (PyBullet benchmarks included).
Training a PPO to balance a pendulum in a fully observable environment.
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
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