Collect & infer-a fresh look at data-efficient reinforcement learning
M Riedmiller, JT Springenberg… - … on Robot Learning, 2022 - proceedings.mlr.press
This position paper proposes a fresh look at Reinforcement Learning (RL) from the
perspective of data-efficiency. RL has gone through three major stages: pure on-line RL …
perspective of data-efficiency. RL has gone through three major stages: pure on-line RL …
How to spend your robot time: Bridging kickstarting and offline reinforcement learning for vision-based robotic manipulation
Reinforcement learning (RL) has been shown to be effective at learning control from
experience. However, RL typically requires a large amount of online interaction with the …
experience. However, RL typically requires a large amount of online interaction with the …
Skills: Adaptive skill sequencing for efficient temporally-extended exploration
The ability to effectively reuse prior knowledge is a key requirement when building general
and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common …
and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common …
Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities
Contemporary artificial intelligence systems exhibit rapidly growing abilities accompanied by
the growth of required resources, expansive datasets and corresponding investments into …
the growth of required resources, expansive datasets and corresponding investments into …