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[HTML][HTML] Applications of reinforcement learning in energy systems
Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …
renewable energy technologies and improve efficiencies, leading to the integration of many …
Deep reinforcement learning: A brief survey
Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence
(AI) and represents a step toward building autonomous systems with a higher-level …
(AI) and represents a step toward building autonomous systems with a higher-level …
Learning agile soccer skills for a bipedal robot with deep reinforcement learning
We investigated whether deep reinforcement learning (deep RL) is able to synthesize
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …
Rvt: Robotic view transformer for 3d object manipulation
For 3D object manipulation, methods that build an explicit 3D representation perform better
than those relying only on camera images. But using explicit 3D representations like voxels …
than those relying only on camera images. But using explicit 3D representations like voxels …
Unidexgrasp++: Improving dexterous gras** policy learning via geometry-aware curriculum and iterative generalist-specialist learning
We propose a novel, object-agnostic method for learning a universal policy for dexterous
object gras** from realistic point cloud observations and proprioceptive information under …
object gras** from realistic point cloud observations and proprioceptive information under …
Contrastive learning as goal-conditioned reinforcement learning
In reinforcement learning (RL), it is easier to solve a task if given a good representation.
While deep RL should automatically acquire such good representations, prior work often …
While deep RL should automatically acquire such good representations, prior work often …
Multi-task learning with deep neural networks: A survey
M Crawshaw - arxiv preprint arxiv:2009.09796, 2020 - arxiv.org
Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are
simultaneously learned by a shared model. Such approaches offer advantages like …
simultaneously learned by a shared model. Such approaches offer advantages like …