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Found 6 papers, 3 papers with code
Hierarchical Reinforcement Learning with Timed Subgoals
Hierarchical reinforcement learning (HRL) holds great potential for sample-efficient learning on challenging long-horizon tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
To coordinate the efforts of the research community toward tackling this problem, we propose a benchmark including: i) a large collection of data for offline learning from a dexterous manipulation platform on two tasks, obtained with capable RL agents trained in simulation; ii) the option to execute learned policies on a real-world robotic system and a simulation for efficient debugging.
Direct Advantage Estimation
The predominant approach in reinforcement learning is to assign credit to actions based on the expected return.
Accelerated physical emulation of Bayesian inference in spiking neural networks
The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices.
Improving Behavioural Cloning with Positive Unlabeled Learning
Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems.
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and benchmark algorithms.
