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Deep reinforcement learning at the edge of the statistical precipice
Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing
their relative performance on a large suite of tasks. Most published results on deep RL …
their relative performance on a large suite of tasks. Most published results on deep RL …
A review of reinforcement learning in chemistry
The growth of machine learning as a tool for research in computational chemistry is well
documented. For many years, this growth was heavily driven by the paradigms of supervised …
documented. For many years, this growth was heavily driven by the paradigms of supervised …
Language as a cognitive tool to imagine goals in curiosity driven exploration
Developmental machine learning studies how artificial agents can model the way children
learn open-ended repertoires of skills. Such agents need to create and represent goals …
learn open-ended repertoires of skills. Such agents need to create and represent goals …
Measuring the reliability of reinforcement learning algorithms
Lack of reliability is a well-known issue for reinforcement learning (RL) algorithms. This
problem has gained increasing attention in recent years, and efforts to improve it have …
problem has gained increasing attention in recent years, and efforts to improve it have …
Exploring safer behaviors for deep reinforcement learning
E Marchesini, D Corsi, A Farinelli - … of the AAAI Conference on Artificial …, 2022 - ojs.aaai.org
Abstract We consider Reinforcement Learning (RL) problems where an agent attempts to
maximize a reward signal while minimizing a cost function that models unsafe behaviors …
maximize a reward signal while minimizing a cost function that models unsafe behaviors …
A domain-agnostic approach for characterization of lifelong learning systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art
systems lack robustness to “real world” events, where the input distributions and tasks …
systems lack robustness to “real world” events, where the input distributions and tasks …
Grounding language to autonomously-acquired skills via goal generation
We are interested in the autonomous acquisition of repertoires of skills. Language-
conditioned reinforcement learning (LC-RL) approaches are great tools in this quest, as they …
conditioned reinforcement learning (LC-RL) approaches are great tools in this quest, as they …
Semantic helm: A human-readable memory for reinforcement learning
F Paischer, T Adler, M Hofmarcher… - Advances in Neural …, 2023 - proceedings.neurips.cc
Reinforcement learning agents deployed in the real world often have to cope with partially
observable environments. Therefore, most agents employ memory mechanisms to …
observable environments. Therefore, most agents employ memory mechanisms to …
Universal morphology control via contextual modulation
Learning a universal policy across different robot morphologies can significantly improve
learning efficiency and generalization in continuous control. However, it poses a challenging …
learning efficiency and generalization in continuous control. However, it poses a challenging …
Deep reinforcement learning for navigation in AAA video games
In video games, non-player characters (NPCs) are used to enhance the players' experience
in a variety of ways, eg, as enemies, allies, or innocent bystanders. A crucial component of …
in a variety of ways, eg, as enemies, allies, or innocent bystanders. A crucial component of …