​Neural MMO — A Massively Multiagent Game Environment

By OpenAI: https://blog.openai.com/neural-mmo/

- Code: https://github.com/openai/neural-mmo
- 3D Client: https://github.com/jsuarez5341/neural-mmo-client

#artificialintelligence #deeplearning #multiagent #reinforcementlearning

🔗 Neural MMO - A Massively Multiagent Game Environment
We’re releasing our Neural MMO - a massively multiagent game environment for reinforcement learning agents.

🎥 Deep Q learning is Easy in PyTorch (Tutorial)
👁 1 раз ⏳ 2055 сек.
Deep Q Learning w/ Pytorch: https://youtu.be/RfNxXlO6BiA
Where to find data for Deep Learning: https://youtu.be/9oW3WfKk6d4

#DeepQLearning #PyTorch #ReinforcementLearning

In this tutorial you will code up the simplest possible deep q network in PyTorch. We'll also correct some minor errors from previous videos, which were rather subtle.

You'll see just how easy it is to implement a deep Q network in Pytorch and beat the lunar lander environment. The agent goes from crashing on the lunar surface to landin

​Useful Models for Robot Learning
Slides by Marc Deisenroth : https://deisenroth.cc/talks/2019-12-14-neurips-ws.pdf
#ReinforcementLearning #Robotics #MetaLearning

🔗

​Deep Learning Drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
GitHub by Marimuthu Kalimuthu: https://github.com/kmario23/deep-learning-drizzle
Webpage: https://deep-learning-drizzle.github.io
#artificialintelligence #deeplearning #machinelearning #reinforcementlearning

🔗 kmario23/deep-learning-drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!! - kmario23/deep-learning-drizzle

​TRFL : TensorFlow Reinforcement Learning
A library of reinforcement learning building blocks
By DeepMind: https://github.com/deepmind/trfl
#DeepLearning #TensorFlow #ReinforcementLearning

🔗 deepmind/trfl
TensorFlow Reinforcement Learning. Contribute to deepmind/trfl development by creating an account on GitHub.

Introduction to Reinforcement Learning

By DeepMind: https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM- OYHWgPebj2MfCFzFObQ
#DeepLearning #ReinforcementLearning #Robotics

🎥 RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learning
👁 1 раз ⏳ 5293 сек.
#Reinforcement Learning Course by David Silver# Lecture 1: Introduction to Reinforcement Learning

#Slides and more info about the course: https://goo.gl/vUiyjq

​Suphx: Mastering Mahjong with Deep Reinforcement Learning
Li et al.: https://arxiv.org/abs/2003.13590
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

🔗 Suphx: Mastering Mahjong with Deep Reinforcement Learning
Artificial Intelligence (AI) has achieved great success in many domains, and game AI is widely regarded as its beachhead since the dawn of AI. In recent years, studies on game AI have gradually evolved from relatively simple environments (e.g., perfect-information games such as Go, chess, shogi or two-player imperfect-information games such as heads-up Texas hold'em) to more complex ones (e.g., multi-player imperfect-information games such as multi-player Texas hold'em and StartCraft II). Mahjong is a popular multi-player imperfect-information game worldwide but very challenging for AI research due to its complex playing/scoring rules and rich hidden information. We design an AI for Mahjong, named Suphx, based on deep reinforcement learning with some newly introduced techniques including global reward prediction, oracle guiding, and run-time policy adaptation. Suphx has demonstrated stronger performance than most top human players in terms of stable rank and is rated above 99.99% of all the officially ranked human players in the Tenhou platform. This is the first time that a computer program outperforms most top human players in Mahjong.

📃 TStarBot-X

TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game
Han et al.: https://arxiv.org/abs/2011.13729
#ReinforcementLearning #ArtificialIntelligence #StarCraft

📃 SLM Lab

SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning
Loon et al.: https://arxiv.org/abs/1912.12482v1
#DeepLearning #OpenAIGym #ReinforcementLearning

📃 TensorTrade

TensorTrade
An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents: https://github.com/tensortrade-org/tensortrade
#OpenAIGym #ReinforcementLearning #Trading

📃 MARS-Gym

MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces
Santana et al.: https://arxiv.org/abs/2010.07035v1
H / T : Anderson Soares
#OpenAIGym #RecommenderSystems #ReinforcementLearning

📃 How to Train Your Robot with Deep Reinforcement Learning

How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned
Ibarz et al.: https://arxiv.org/abs/2102.02915
#Robotics #MachineLearning #ReinforcementLearning

📃 State Entropy Maximization with Random Encoders for Efficient Exploration

State Entropy Maximization with Random Encoders for Efficient Exploration
Seo et al.: https://arxiv.org/abs/2102.09430
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

 Data Science / Machine Learning / AI / Big Data (VK)

Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings

Chen et al.: https://arxiv.org/abs/2103.02886

#ArtificialIntelligence #DeepLearning #ReinforcementLearning