A review of cooperative multi-agent deep reinforcement learning
A Oroojlooy, D Hajinezhad - Applied Intelligence, 2023 - Springer
Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …
systems in recent years. The aim of this review article is to provide an overview of recent …
A survey of reinforcement learning algorithms for dynamically varying environments
S Padakandla - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
Reinforcement learning (RL) algorithms find applications in inventory control, recommender
systems, vehicular traffic management, cloud computing, and robotics. The real-world …
systems, vehicular traffic management, cloud computing, and robotics. The real-world …
Dense reinforcement learning for safety validation of autonomous vehicles
One critical bottleneck that impedes the development and deployment of autonomous
vehicles is the prohibitively high economic and time costs required to validate their safety in …
vehicles is the prohibitively high economic and time costs required to validate their safety in …
Stable-baselines3: Reliable reinforcement learning implementations
STABLE-BASELINES3 provides open-source implementations of deep reinforcement
learning (RL) algorithms in Python. The implementations have been benchmarked against …
learning (RL) algorithms in Python. The implementations have been benchmarked against …
Cleanrl: High-quality single-file implementations of deep reinforcement learning algorithms
CleanRL is an open-source library that provides high-quality single-file implementations of
Deep Reinforcement Learning (DRL) algorithms. These single-file implementations are self …
Deep Reinforcement Learning (DRL) algorithms. These single-file implementations are self …
Pettingzoo: Gym for multi-agent reinforcement learning
This paper introduces the PettingZoo library and the accompanying Agent Environment
Cycle (" AEC") games model. PettingZoo is a library of diverse sets of multi-agent …
Cycle (" AEC") games model. PettingZoo is a library of diverse sets of multi-agent …
Deep reinforcement learning for intelligent transportation systems: A survey
Latest technological improvements increased the quality of transportation. New data-driven
approaches bring out a new research direction for all control-based systems, eg, in …
approaches bring out a new research direction for all control-based systems, eg, in …
Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning
Driving safely requires multiple capabilities from human and intelligent agents, such as the
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …
Structured state space models for in-context reinforcement learning
Structured state space sequence (S4) models have recently achieved state-of-the-art
performance on long-range sequence modeling tasks. These models also have fast …
performance on long-range sequence modeling tasks. These models also have fast …
Monotonic value function factorisation for deep multi-agent reinforcement learning
In many real-world settings, a team of agents must coordinate its behaviour while acting in a
decentralised fashion. At the same time, it is often possible to train the agents in a …
decentralised fashion. At the same time, it is often possible to train the agents in a …