#Policy-gradient
Showing 23 of 23 repositories tagged #policy-gradient, ranked by stars
An elegant PyTorch deep reinforcement learning library.
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
Minimal and Clean Reinforcement Learning Examples
Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..
A curated list of Monte Carlo tree search papers with implementations.
A simplified PyTorch implementation of "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient." (Yu, Lantao, et al.)
Multiple implementations for abstractive text summurization , using google colab
Structural implementation of RL key algorithms
lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
📚 List of Top-tier Conference Papers on Reinforcement Learning (RL)，including: NeurIPS, ICML, AAAI, IJCAI, AAMAS, ICLR, ICRA, etc.
A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading
PyTorch implementations of algorithms from "Reinforcement Learning: An Introduction by Sutton and Barto", along with various RL research papers.
A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
A comprehensive guide to Reinforcement Learning
Explorer is a PyTorch reinforcement learning framework for exploring new ideas.
Collection of Deep Reinforcement Learning Algorithms implemented in PyTorch.
A course on Deep Reinforcement Learning in Computer Vision. Visit Website:
This Reinforcement learning agent uses Policy-Gradient method to trade the market
NYCU Intro2AI Final Project
This project aims to select a supervised algorithm that can predict stock prices basing on historical data and use the predictor generated to form trading strategies.