TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
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Updated
Dec 2, 2024 - Python
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Related papers for reinforcement learning, including classic papers and latest papers in top conferences
📚 List of Top-tier Conference Papers on Reinforcement Learning (RL),including: NeurIPS, ICML, AAAI, IJCAI, AAMAS, ICLR, ICRA, etc.
JAX-accelerated Meta-Reinforcement Learning Environments Inspired by XLand and MiniGrid 🏎️
Reading list for adversarial perspective and robustness in deep reinforcement learning.
Implementation of the two-step-task as described in "Prefrontal cortex as a meta-reinforcement learning system" and "Learning to Reinforcement Learn".
Implementation of 'RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning'
XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning
Code snippets of Meta Reinforcement Learning algorithms
Code for paper "Model-based Adversarial Meta-Reinforcement Learning" (https://arxiv.org/abs/2006.08875)
PyTorch implementation of Episodic Meta Reinforcement Learning on variants of the "Two-Step" task. Reproduces the results found in three papers. Check the ReadMe for more details!
A collection of Meta-Reinforcement Learning algorithms in PyTorch
🎉🎨 This repository contains a reading list of papers with code on **Meta-Learning** and ***Meta-Reinforcement-Learning*
A Survey Analyzing Generalization in Deep Reinforcement Learning
The proceedings of top conference in 2023 on the topic of Reinforcement Learning (RL), including: AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS and more.
Implementation of Improving Generalization for Neural Adaptive Video Streaming via Meta Reinforcement Learning - N. Kan et al. (ACM MM22)
Code of the paper: Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value Function
PyTorch implementation of two variants of the Harlow visual fixation task (PsychLab and 1D version). Reproduces the results found in two papers. Check the ReadMe for more details!
Code for the "Evolving Reservoirs for Meta Reinforcement Learning" paper
Code for the paper "Meta-Reinforcement Learning by Tracking Task Non-stationarity" (IJCAI 2021)
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