CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
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Updated
Oct 28, 2020 - Python
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
This is the pytorch implementation of Hindsight Experience Replay (HER) - Experiment on all fetch robotic environments.
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Official PyTorch code for "Recurrent Off-policy Baselines for Memory-based Continuous Control" (DeepRL Workshop, NeurIPS 21)
ExORL: Exploratory Data for Offline Reinforcement Learning
⚡ Flashbax: Accelerated Replay Buffers in JAX
Causal RL: Reverse-Environment Network Integrated Actor-Critic Algorithm
Actor Prioritized Experience Replay
PyTorch implementation of "Sample-efficient Imitation Learning via Generative Adversarial Nets"
TensorFlow implementation of "Sample-efficient Imitation Learning via Generative Adversarial Nets"
PyTorch implementation of our work: "Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learning"
My content of CS294 Deep Reinforcement Learning course, conduced by Sergey Levine from UC Berkeley.
PyTorch implementation of "Sample-efficient Imitation Learning via Generative Adversarial Nets"
Contains PyTorch Implementation of the following off policy actor critic algorithms
Safe and Robust Experience Sharing for Deterministic Policy Gradient Algorithms
A novel method to incorporate existing policy (Rule-based control) with Reinforcement Learning.
Sample Policy Gradient
An Optimistic Approach to the Q-Network Error in Actor-Critic Methods
PyTorch implementation of our work: "Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learning"
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