A review of safe reinforcement learning: Methods, theory and applications
Reinforcement learning (RL) has achieved tremendous success in many complex decision
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …
Multi-agent reinforcement learning is a sequence modeling problem
Large sequence models (SM) such as GPT series and BERT have displayed outstanding
performance and generalization capabilities in natural language process, vision and …
performance and generalization capabilities in natural language process, vision and …
On Transforming Reinforcement Learning With Transformers: The Development Trajectory
Transformers, originally devised for natural language processing (NLP), have also produced
significant successes in computer vision (CV). Due to their strong expression power …
significant successes in computer vision (CV). Due to their strong expression power …
Towards human-level bimanual dexterous manipulation with reinforcement learning
Achieving human-level dexterity is an important open problem in robotics. However, tasks of
dexterous hand manipulation even at the baby level are challenging to solve through …
dexterous hand manipulation even at the baby level are challenging to solve through …
Safe multi-agent reinforcement learning for multi-robot control
A challenging problem in robotics is how to control multiple robots cooperatively and safely
in real-world applications. Yet, developing multi-robot control methods from the perspective …
in real-world applications. Yet, developing multi-robot control methods from the perspective …
Towards a standardised performance evaluation protocol for cooperative marl
R Gorsane, O Mahjoub, RJ de Kock… - Advances in …, 2022 - proceedings.neurips.cc
Multi-agent reinforcement learning (MARL) has emerged as a useful approach to solving
decentralised decision-making problems at scale. Research in the field has been growing …
decentralised decision-making problems at scale. Research in the field has been growing …
Offline pre-trained multi-agent decision transformer
Offline reinforcement learning leverages previously collected offline datasets to learn
optimal policies with no necessity to access the real environment. Such a paradigm is also …
optimal policies with no necessity to access the real environment. Such a paradigm is also …
Multi-agent constrained policy optimisation
Developing reinforcement learning algorithms that satisfy safety constraints is becoming
increasingly important in real-world applications. In multi-agent reinforcement learning …
increasingly important in real-world applications. In multi-agent reinforcement learning …
[PDF][PDF] Heterogeneous-agent reinforcement learning
The necessity for cooperation among intelligent machines has popularised cooperative multi-
agent reinforcement learning (MARL) in AI research. However, many research endeavours …
agent reinforcement learning (MARL) in AI research. However, many research endeavours …
Ace: Cooperative multi-agent q-learning with bidirectional action-dependency
Multi-agent reinforcement learning (MARL) suffers from the non-stationarity problem, which
is the ever-changing targets at every iteration when multiple agents update their policies at …
is the ever-changing targets at every iteration when multiple agents update their policies at …