Reinforcement learning algorithms: A brief survey
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 …
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
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 …
Masked visual pre-training for motor control
This paper shows that self-supervised visual pre-training from real-world images is effective
for learning motor control tasks from pixels. We first train the visual representations by …
for learning motor control tasks from pixels. We first train the visual representations by …
Mastering atari games with limited data
Reinforcement learning has achieved great success in many applications. However, sample
efficiency remains a key challenge, with prominent methods requiring millions (or even …
efficiency remains a key challenge, with prominent methods requiring millions (or even …
Mastering visual continuous control: Improved data-augmented reinforcement learning
We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual
continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data …
continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data …
Curl: Contrastive unsupervised representations for reinforcement learning
Abstract We present CURL: Contrastive Unsupervised Representations for Reinforcement
Learning. CURL extracts high-level features from raw pixels using contrastive learning and …
Learning. CURL extracts high-level features from raw pixels using contrastive learning and …
Contrastive learning as goal-conditioned reinforcement learning
In reinforcement learning (RL), it is easier to solve a task if given a good representation.
While deep RL should automatically acquire such good representations, prior work often …
While deep RL should automatically acquire such good representations, prior work often …
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
We propose a simple data augmentation technique that can be applied to standard model-
free reinforcement learning algorithms, enabling robust learning directly from pixels without …
free reinforcement learning algorithms, enabling robust learning directly from pixels without …