High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
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Updated
May 21, 2024 - Python
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
A PyTorch library for building deep reinforcement learning agents.
[ICRA'23] Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot
Curiosity-driven Exploration by Self-supervised Prediction
Code for Hands On Intelligent Agents with OpenAI Gym book to get started and learn to build deep reinforcement learning agents using PyTorch
Recurrent and multi-process PyTorch implementation of deep reinforcement Actor-Critic algorithms A2C and PPO
Training a Reinforcement Learning Agent to Play Flappy Bird.
Example A2C implementation with ReLAx
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
Implementations of deep reinforcement learning algorithms.
This repository contains my assignment solutions for the Deep Learning course (M2177.003100_002) offered by Seoul National University (Fall 2019).
The pytorch implemetation of a2c
PyTorch implementation of some reinforcement learning algorithms: A2C, PPO, Behavioral Cloning from Observation (BCO), GAIL.
Scalable, event-driven, deep-learning-friendly backtesting library
Code accompanying the blog post "Deep Reinforcement Learning with TensorFlow 2.1"
Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch
First Place Reinforcement Learning solution code and a writeup for the AI RoboSoccer Competition.
The friendly robot that beats you in Yahtzee 🤖 🎲
Official implementation of the AAAI 2021 paper Deep Bayesian Quadrature Policy Optimization.
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