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 …

In this paper, we present Tianshou, a highly modularized Python library for deep
reinforcement learning (DRL) that uses PyTorch as its backend. Tianshou intends to be …

We study the benefit of sharing representations among tasks to enable the effective use of
deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption …

Today's environmental concerns, particularly those related to global warming, have sparked
a drive for the usage of renewable energy sources. One of the most significant sources of …

Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence
(AI) by endowing autonomous systems with high levels of understanding of the real world …

Autonomous robotic assembly requires a well-orchestrated sequence of high-level actions
and smooth manipulation executions. Learning to assemble complex 3D structures remains …

Mobile Manipulation (MM) systems are ideal candidates for taking up the role of personal
assistants in unstructured real-world environments. Among other challenges, Mobile …

This paper addresses the problem of model-free reinforcement learning for Robust Markov
Decision Process (RMDP) with large state spaces. The goal of the RMDPs framework is to …

Reinforcement learning in robotics is extremely challenging due to many practical issues,
including safety, mechanical constraints, and wear and tear. Typically, these issues are not …

Classical value iteration approaches are not applicable to environments with continuous
states and actions. For such environments, the states and actions are usually discretized …