Add to ORKGUnderstanding the long-term impact of algorithmic interventions on society is vital to achieving responsible AI. Traditional evaluation strategies often fall short due to the complex, adaptive and dynamic nature of society. While reinforcement learning (RL) can be a powerful approach for optimizing decisions in dynamic settings, the difficulty of realistic environment design remains a barrier to building robust agents that perform well in practical settings. To address this issue we tap into the field of system dynamics (SD) as a complementary method that incorporates collaborative simulation model specification practices. We introduce SDGym, a low-code library built on the OpenAI Gym framework which enables the generation of custom RL envi...
Reinforcement learning (RL) algorithms have proven transformative in a range of domains. To tackle r...
Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex...
Advances in reinforcement learning research have demonstrated the ways in which different agent-base...
This paper addresses the dire need for a platform that efficiently provides a framework for running ...
Robots moving safely and in a socially compliant manner in dynamic human environments is an essentia...
Recent progress in reinforcement learning (RL) has started producing generally capable agents that c...
Typically in reinforcement learning, agents are trained and evaluated on the same environment. Conse...
Deep reinforcement learning (RL) provides powerful methods for training optimal sequential decision-...
Adapting a Reinforcement Learning (RL) agent to an unseen environment is a difficult task due to typ...
Reinforcement learning (RL) is a promising solution for autonomous vehicles to deal with complex and...
Traffic simulators are used to generate data for learning in intelligent transportation systems (ITS...
Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle (AGV) navigation utili...
We propose a method for meta-learning reinforcement learning algorithms by searching over the space ...
Reinforcement Learning (RL) is a machine learning technique that enables artificial agents to learn ...
Economic dynamic models of climate change usually involve many variables, complex dynamics and uncer...
Reinforcement learning (RL) algorithms have proven transformative in a range of domains. To tackle r...
Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex...
Advances in reinforcement learning research have demonstrated the ways in which different agent-base...
This paper addresses the dire need for a platform that efficiently provides a framework for running ...
Robots moving safely and in a socially compliant manner in dynamic human environments is an essentia...
Recent progress in reinforcement learning (RL) has started producing generally capable agents that c...
Typically in reinforcement learning, agents are trained and evaluated on the same environment. Conse...
Deep reinforcement learning (RL) provides powerful methods for training optimal sequential decision-...
Adapting a Reinforcement Learning (RL) agent to an unseen environment is a difficult task due to typ...
Reinforcement learning (RL) is a promising solution for autonomous vehicles to deal with complex and...
Traffic simulators are used to generate data for learning in intelligent transportation systems (ITS...
Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle (AGV) navigation utili...
We propose a method for meta-learning reinforcement learning algorithms by searching over the space ...
Reinforcement Learning (RL) is a machine learning technique that enables artificial agents to learn ...
Economic dynamic models of climate change usually involve many variables, complex dynamics and uncer...
Reinforcement learning (RL) algorithms have proven transformative in a range of domains. To tackle r...
Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex...
Advances in reinforcement learning research have demonstrated the ways in which different agent-base...