OpenAI's ES used in a feudal hrl style
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Updated
Jan 24, 2022 - Julia
OpenAI's ES used in a feudal hrl style
Pytorch code for Hierarchical Latent Space Learning (HLSL)
Spring 2021 - CSE 574 Project
Pure-python-based, lightweight 2d-navigation environments.
Anchor: The achieved goal to replace the subgoal for hierarchical reinforcement learning
Implementation of Option-Critic Algorithm - https://arxiv.org/abs/1609.05140
Reinforcement learning algorithms constrained by a partial program
Python implementation of Hierarchies of Abstract Machine (HAM) as a python coroutine. (Abandoned, new repo at Juno-T/pyham)
HRL envs from Data efficient hierarchical reinforcement learning in Julia using Lyceum MuJoCo
A collection of useful environments for testing Reinforcement Learning algorithms. Designed (mostly) with discrete, graph-based methods in mind.
HierLearning is a C++11 implementation of a multi-agent, hierarchical reinforcement learning system for sequential decision problems.
Modular Deep RL infrastructure in PyTorch
This repo implements the HIRO algorithm for Hierarchical Reinforcement Learning in the original environment using Tensorflow 2.
Posterior Goal Sampling for Hierarchical Reinforcement Learning
Tasks with combinatorial structure embedded in MuJoCo robotics environments.
An end-to-end differentiable hierarchical reinforcement learning agent based on continuous sub-policy attention.
hdrqn
Implementation of STAR from the paper "Reconciling Spatial and Temporal Abstractions for Goal Representation" (ICLR 2024)
An interface for hierarchical environments.
In this paper we re-define MAXQ and the taxi environment and Implement them in R. We then apply Qlearning to the same problem. Our conclusion is that MAXQ works as good as Qlearning for this problem. Our aim is illustrate the advantages of using hierarchical reinforcement learning methods.
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