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PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Lightning ⚡️ fast forecasting with statistical and econometric models.
Scalable and user friendly neural 🧠 forecasting algorithms.
An index of algorithms for learning causality with data
Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.
S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics
RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).
Continual learning baselines and strategies from popular papers, using Avalanche. We include EWC, SI, GEM, AGEM, LwF, iCarl, GDumb, and other strategies.
Mirror of Stable-Baselines: a fork of OpenAI Baselines, implementations of reinforcement learning algorithms
Multi-turn dialogue baselines written in PyTorch
Re-implementations of SOTA RL algorithms.
Baseline classifiers on the polluted MNIST dataset, SJTU CS420 course project
Some common recommendation system baseline, with description and link.
An OpenAI Gym and Reinforcement Learning Library built in Typescript 🤖
Deep Developmental Reinforcement Learning
This work considers combine multi-tricks with highway network to achieve traffic flow prediction accurately.
