Implementation of Inverse Reinforcement Learning (IRL) algorithms in Python/Tensorflow. Deep MaxEnt, MaxEnt, LPIRL
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Updated
May 10, 2024 - Python
Implementation of Inverse Reinforcement Learning (IRL) algorithms in Python/Tensorflow. Deep MaxEnt, MaxEnt, LPIRL
Implementation of Inverse Reinforcement Learning Algorithm on a toy car in a 2D world problem, (Apprenticeship Learning via Inverse Reinforcement Learning Abbeel & Ng, 2004)
🤖 The Full Process Python Package for Robot Learning from Demonstration and Robot Manipulation
Implementation of the paper "Overcoming Exploration in Reinforcement Learning with Demonstrations" Nair et al. over the HER baselines from OpenAI
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer; and “Good Robot!”: Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer
Integrating learning and task planning for robots with Keras, including simulation, real robot, and multiple dataset support.
A robot learning from demonstration framework that trains a recurrent neural network for autonomous task execution
Kernelized Movement Primitives (KMP)
Train a robot to see the environment and autonomously perform different tasks
Dynamic Motion Primitives
An implementation of Deep Q-Learning from Demonstrations (DQfD) for playing Atari 2600 video games
Combined Learning from Demonstration and Motion Planning
[ICLR 2022 Spotlight] Code for Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration
Implementation of the paper "Human-like Planning for Reaching in Cluttered Environments" (ICRA 2020)
A framework and method to jointly learn a (neural) control objective function and a time-warping function only from sparse demonstrations or waypoints.
This repository contains the source code for our paper: "Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation", accepted to IROS-2022. For more details, please refer to our project website at https://sites.google.com/view/san-fapl.
[NeurIPS 2022] Code for Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments
Augmented Joint-space Task-oriented Dynamical Systems
Stable dynamical system learning using Euclideanizing flows
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