From affb98763b17580cb2eb47662c5869deb8e49804 Mon Sep 17 00:00:00 2001 From: Florian Felten Date: Thu, 14 Mar 2024 17:24:23 +0100 Subject: [PATCH] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 4aeb9f08..6c107d77 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ This repository aims to contain reliable MORL algorithms implementations in PyTo It strictly follows [MO-Gymnasium](https://github.com/Farama-Foundation/mo-gymnasium) API, which differs from the standard [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) API only in that the environment returns a numpy array as the reward. -For details on multi-objective MDPs (MOMDPs) and other MORL definitions, we suggest reading [A practical guide to multi-objective reinforcement learning and planning](https://link.springer.com/article/10.1007/s10458-022-09552-y). +For details on multi-objective MDPs (MOMDPs) and other MORL definitions, we suggest reading [A practical guide to multi-objective reinforcement learning and planning](https://link.springer.com/article/10.1007/s10458-022-09552-y). An overview of some techniques used in various MORL algorithms is also provided in [Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework](https://doi.org/10.1613/jair.1.15702). A tutorial on MO-Gymnasium and MORL-Baselines is also available: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ByjuUp8-CJeh1giPOACqPGiglPxDnlSq?usp=sharing) @@ -47,7 +47,7 @@ A tutorial on MO-Gymnasium and MORL-Baselines is also available: [![Open in Cola | **Name** | Single/Multi-policy | ESR/SER | Observation space | Action space | Paper | |----------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------|-----------------------------|-------------------|-----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------| | [GPI-LS + GPI-PD](https://github.com/LucasAlegre/morl-baselines/blob/main/morl_baselines/multi_policy/gpi_pd/gpi_pd.py) | Multi | SER | Continuous | Discrete / Continuous | [Paper and Supplementary Materials](https://arxiv.org/abs/2301.07784) | -| [MORL/D](https://github.com/LucasAlegre/morl-baselines/blob/main/morl_baselines/multi_policy/morld/morld.py) | Multi | / | / | / | [Paper](https://arxiv.org/abs/2311.12495) | +| [MORL/D](https://github.com/LucasAlegre/morl-baselines/blob/main/morl_baselines/multi_policy/morld/morld.py) | Multi | / | / | / | [Paper](https://doi.org/10.1613/jair.1.15702) | | [Envelope Q-Learning](https://github.com/LucasAlegre/morl-baselines/blob/main/morl_baselines/multi_policy/envelope/envelope.py) | Multi | SER | Continuous | Discrete | [Paper](https://arxiv.org/pdf/1908.08342.pdf) | | [CAPQL](https://github.com/LucasAlegre/morl-baselines/blob/main/morl_baselines/multi_policy/capql/capql.py) | Multi | SER | Continuous | Continuous | [Paper](https://openreview.net/pdf?id=TjEzIsyEsQ6) | | [PGMORL](https://github.com/LucasAlegre/morl-baselines/blob/main/morl_baselines/multi_policy/pgmorl/pgmorl.py) [1](#f1) | Multi | SER | Continuous | Continuous | [Paper](https://people.csail.mit.edu/jiex/papers/PGMORL/paper.pdf) / [Supplementary Materials](https://people.csail.mit.edu/jiex/papers/PGMORL/supp.pdf) |