Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms
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Updated
Dec 11, 2022 - Python
Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms
For deep RL and the future of AI.
The codes of paper "Long Text Generation via Adversarial Training with Leaked Information" on AAAI 2018. Text generation using GAN and Hierarchical Reinforcement Learning.
📚 List of Top-tier Conference Papers on Reinforcement Learning (RL),including: NeurIPS, ICML, AAAI, IJCAI, AAMAS, ICLR, ICRA, etc.
PyTorch implementation of Hierarchical Actor Critic (HAC) for OpenAI gym environments
Our codebase trials provide an implementation of the Select and Trade paper, which proposes a new paradigm for pair trading using hierarchical reinforcement learning. It includes the code for the proposed method and experimental results on real-world stock data to demonstrate its effectiveness.
Deep Hierarchical Planning from Pixels
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
Notes and comments about Deep Reinforcement Learning papers
[AAAI 2024] GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time
A Tensorflow implementation of the Option-Critic Architecture
Heterogeneous Hierarchical Multi Agent Reinforcement Learning for Air Combat
Hierarchical-DQN in pytorch (not actively maintained)
Code for CoRL 2019 paper: HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators
"Hierarchical Reinforcement Learning for Integrated Recommendation" (AAAI 2021) https://ojs.aaai.org/index.php/AAAI/article/view/16580
The Reinforcement-Learning-Related Papers of ICLR 2019
Official implementation of H-TSP (AAAI2023)
PyTorch code accompanying the paper "Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning" (NeurIPS 2020 spotlight).
Hierarchical Online Planning and Reinforcement Learning on Taxi
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