Machine Learning is becoming a common technique to address robotics tasks. This repository intends cover this usage from a broad point of view.

• Auto-Encoding Variational Bayes, by Kingma et Al.
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models, by Rezende et Al.
• Variational Inference with Normalizing Flows, by Rezende et Al.
• Improved Variational Inference with Inverse Autoregressive Flows, By Kingma et Al.
• Gradient Estimation Using Stochastic Computation Graphs by Schulman et Al.
• Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data by Karl et Al.
• Deep Kalman Filters by Krishnan et Al.
• Variational Inference: Foundations and Modern Methods, byu Blei et Al.

• Deep Q-Learning: Human-level control through deep reinforcement learning, by Mnih et Al.
• DDPG: Continuous control with Deep Reinforcement Learning, by Lillicrap et Al.
• Prioritized Experience Replay, by Schaul et Al.
• Auxiliary tasks: Reinforcement learning with unsupervised auxiliary tasks, by Jaderberg et Al.
• Emergence of Locomotion Behaviours in Rich Environments, by Heess et Al.

• Deep RL that matters, by Henderson et Al.
• Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control, by Islam et Al.

• REINFORCE: Simple statistical gradient-following algorithms for connectionist reinforcement learning, Williams et Al.
• Policy Gradient Theorem: Policy Gradient Methods for Reinforcement Learning with Function Approximation, Sutton et Al.
• Deterministic Policy Gradient Algorithms, Silver et Al.

• Policy search suvery: Reinforcement learning of motor skills with policy gradients, by Peters and Schaal.
• Guided policy search, by Levine et Al.

• Intrinsically Motivated Multi-Task Reinforcement Learning, by Forestier and Oudeyer.

• Automated curriculum learning, by Graves et Al.

• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, by Finn and Al.

• OpenAI Gym: A Python library providing many simulation environments.
• OpenAI baselines: Implementations of Deep Reinforcement Learning algorithms by experts.
• Explauto: A library to perform intrinsically motivated exploration.
• Guided Policy Search: Implementation of the Guided Policy Search algorithm.
• Keras-RL: A keras-compatible Deep Reinforcement Learning framework (DQN, SARSA, DDPG...).
• Deepmind DQN: Deepmind's implementation used for the Nature paper.
• Devsisters DQN: A nice DQN implementation.

• Best pratices in implementing Deep RL algorithms, as part of a blog post.
• A note about gradient clipping, by Karpathy. Further explained in a blog post.

• MuJoCo: The reference. Closed-source
• OpenAI Roboschool: A Mujoco clone in bullet, open-source.
• Gazebo: A simulator used in the ROS suite.
• V-REP: A simulator used with the Poppy project.

• Poppy: An open-source 3D-printed robotic ecosystem (humanoid, torso...)