Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments.
Permutations then serve as target generation orders for training an insertion-based Transformer language model.
First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved.
Corrective interventions while a robot is learning to automate a task provide an intuitive method for a human supervisor to assist the robot and convey information about desired behavior.
One strategy to recover this information is to decode both the content and location of tokens.
This work connects the context-sensitive nature of cognitive control to a method for meta-learning with context-conditioned adaptation.
Researchers and practitioners in the field of reinforcement learning (RL) frequently leverage parallel computation, which has led to a plethora of new algorithms and systems in the last few years.
Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration.
To address this, we propose a new distributed reinforcement learning algorithm, IMPACT.