JORLDY: a fully customizable open source framework for reinforcement learning

• URL: http://arxiv.org/abs/2204.04892v1
• Date: Mon, 11 Apr 2022 06:28:27 GMT
• Title: JORLDY: a fully customizable open source framework for reinforcement learning
• Authors: Kyushik Min, Hyunho Lee, Kwansu Shin, Taehak Lee, Hojoon Lee, Jinwon Choi, Sungho Son
• Abstract summary: Reinforcement Learning (RL) has been actively researched in both academic and industrial fields. JORLDY provides more than 20 widely used RL algorithms which are implemented with Pytorch. JORLDY supports multiple RL environments which include OpenAI gym, Unity ML-Agents, Mujoco, Super Mario Bros and Procgen.
• Score: 3.1864456096282696
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recently, Reinforcement Learning (RL) has been actively researched in both academic and industrial fields. However, there exist only a few RL frameworks which are developed for researchers or students who want to study RL. In response, we propose an open-source RL framework "Join Our Reinforcement Learning framework for Developing Yours" (JORLDY). JORLDY provides more than 20 widely used RL algorithms which are implemented with Pytorch. Also, JORLDY supports multiple RL environments which include OpenAI gym, Unity ML-Agents, Mujoco, Super Mario Bros and Procgen. Moreover, the algorithmic components such as agent, network, environment can be freely customized, so that the users can easily modify and append algorithmic components. We expect that JORLDY will support various RL research and contribute further advance the field of RL. The source code of JORLDY is provided on the following Github: https://github.com/kakaoenterprise/JORLDY

Related papers

• ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL [80.10358123795946]
  We develop a framework for building multi-turn RL algorithms for fine-tuning large language models. Our framework adopts a hierarchical RL approach and runs two RL algorithms in parallel. Empirically, we find that ArCHer significantly improves efficiency and performance on agent tasks.
  arXiv  Detail & Related papers  (2024-02-29T18:45:56Z)
• OpenRL: A Unified Reinforcement Learning Framework [19.12129820612253]
  We present OpenRL, an advanced reinforcement learning (RL) framework. It is designed to accommodate a diverse array of tasks, from single-agent challenges to complex multi-agent systems. It integrates Natural Language Processing (NLP) with RL, enabling researchers to address a combination of RL training and language-centric tasks effectively.
  arXiv  Detail & Related papers  (2023-12-20T12:04:06Z)
• RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark [69.19502244910632]
  Deep reinforcement learning (RL) has shown significant benefits in solving optimization (CO) problems. We introduce RL4CO, a unified benchmark with in-depth library coverage of 23 state-of-the-art methods and more than 20 CO problems. Built on efficient software libraries and best practices in implementation, RL4CO features modularized implementation and flexible configuration of diverse RL algorithms, neural network architectures, inference techniques, and environments.
  arXiv  Detail & Related papers  (2023-06-29T16:57:22Z)
• Godot Reinforcement Learning Agents [10.413185820687021]
  The Godot RL Agents interface allows the design, creation and learning of agent behaviors in challenging 2D and 3D environments. The framework is a versatile tool that allows researchers and game designers the ability to create environments with discrete, continuous and mixed action spaces.
  arXiv  Detail & Related papers  (2021-12-07T11:24:34Z)
• RL-DARTS: Differentiable Architecture Search for Reinforcement Learning [62.95469460505922]
  We introduce RL-DARTS, one of the first applications of Differentiable Architecture Search (DARTS) in reinforcement learning (RL) By replacing the image encoder with a DARTS supernet, our search method is sample-efficient, requires minimal extra compute resources, and is also compatible with off-policy and on-policy RL algorithms, needing only minor changes in preexisting code. We show that the supernet gradually learns better cells, leading to alternative architectures which can be highly competitive against manually designed policies, but also verify previous design choices for RL policies.
  arXiv  Detail & Related papers  (2021-06-04T03:08:43Z)
• Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym [21.4215863934377]
  Reinforcement learning (RL) is a popular machine learning paradigm for game playing, robotics control, and other sequential decision tasks. This article introduces our new open-source RL framework, the Human Input Parsing Platform for Openai Gym (HIPPO Gym)
  arXiv  Detail & Related papers  (2021-02-02T12:56:02Z)
• EasyRL: A Simple and Extensible Reinforcement Learning Framework [3.2173369911280023]
  EasyRL provides an interactive graphical user interface for users to train and evaluate RL agents. EasyRL does not require programming knowledge for training and testing simple built-in RL agents. EasyRL also supports custom RL agents and environments, which can be highly beneficial for RL researchers in evaluating and comparing their RL models.
  arXiv  Detail & Related papers  (2020-08-04T17:02:56Z)
• RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning [108.9599280270704]
  We propose a benchmark called RL Unplugged to evaluate and compare offline RL methods. RL Unplugged includes data from a diverse range of domains including games and simulated motor control problems. We will release data for all our tasks and open-source all algorithms presented in this paper.
  arXiv  Detail & Related papers  (2020-06-24T17:14:51Z)
• The NetHack Learning Environment [79.06395964379107]
  We present the NetHack Learning Environment (NLE), a procedurally generated rogue-like environment for Reinforcement Learning research. We argue that NetHack is sufficiently complex to drive long-term research on problems such as exploration, planning, skill acquisition, and language-conditioned RL. We demonstrate empirical success for early stages of the game using a distributed Deep RL baseline and Random Network Distillation exploration.
  arXiv  Detail & Related papers  (2020-06-24T14:12:56Z)
• MushroomRL: Simplifying Reinforcement Learning Research [60.70556446270147]
  MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a comprehensive and flexible framework to minimize the effort in implementing and testing novel RL methodologies.
  arXiv  Detail & Related papers  (2020-01-04T17:23:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.