TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
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Updated
Jun 4, 2024 - Python
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
📚 List of Top-tier Conference Papers on Reinforcement Learning (RL),including: NeurIPS, ICML, AAAI, IJCAI, AAMAS, ICLR, ICRA, etc.
Related papers for reinforcement learning, including classic papers and latest papers in top conferences
Implementation of the two-step-task as described in "Prefrontal cortex as a meta-reinforcement learning system" and "Learning to Reinforcement Learn".
JAX-accelerated Meta-Reinforcement Learning Environments Inspired by XLand and MiniGrid 🏎️
Implementation of 'RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning'
Code for paper "Model-based Adversarial Meta-Reinforcement Learning" (https://arxiv.org/abs/2006.08875)
Implementation of Improving Generalization for Neural Adaptive Video Streaming via Meta Reinforcement Learning - N. Kan et al. (ACM MM22)
Reading list for adversarial perspective and robustness in deep reinforcement learning.
🎉🎨 This repository contains a reading list of papers with code on **Meta-Learning** and ***Meta-Reinforcement-Learning*
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
Code of the paper: Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value Function
Code for the "Evolving Reservoirs for Meta Reinforcement Learning" paper
Implementation of the paper "MERINA+: Improving Generalization for Neural Video Adaptation via Information-Theoretic Meta-Reinforcement Learning" - N. Kan, et. al., 2023
Code snippets of Meta Reinforcement Learning algorithms
Code for the paper "Meta-Reinforcement Learning by Tracking Task Non-stationarity" (IJCAI 2021)
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!
A Survey Analyzing Generalization in Deep Reinforcement Learning
meta-RL soft actor-critic with BRUNO for task inference
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