Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

In accordance with the rapid proliferation of machine learning (ML) and data management,
ML applications have evolved to encompass all engineering disciplines. Owing to the …

A longstanding goal of the field of AI is a method for learning a highly capable, generalist
agent from diverse experience. In the subfields of vision and language, this was largely …

STABLE-BASELINES3 provides open-source implementations of deep reinforcement
learning (RL) algorithms in Python. The implementations have been benchmarked against …

We introduce a value-based RL agent, which we call BBF, that achieves super-human
performance in the Atari 100K benchmark. BBF relies on scaling the neural networks used …

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 …

In this work we identify the dormant neuron phenomenon in deep reinforcement learning,
where an agent's network suffers from an increasing number of inactive neurons, thereby …

Intelligent agents need to generalize from past experience to achieve goals in complex
environments. World models facilitate such generalization and allow learning behaviors …

Reinforcement Learning (RL) is a continuously growing field that has the potential to
revolutionize many areas of artificial intelligence. However, despite its promise, RL research …

Efficiently navigating a superpressure balloon in the stratosphere requires the integration of
a multitude of cues, such as wind speed and solar elevation, and the process is complicated …