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Found 9 papers, 2 papers with code
Critic Regularized Regression
Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction.
Extracting Scientific Figures with Distantly Supervised Neural Networks
Non-textual components such as charts, diagrams and tables provide key information in many scientific documents, but the lack of large labeled datasets has impeded the development of data-driven methods for scientific figure extraction.
Compositional Transfer in Hierarchical Reinforcement Learning
The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements.
General Reinforcement Learning
Hierarchical Reinforcement Learning
+4
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models
Humans are masters at quickly learning many complex tasks, relying on an approximate understanding of the dynamics of their environments.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
+2
Simple Sensor Intentions for Exploration
In particular, we show that a real robotic arm can learn to grasp and lift and solve a Ball-in-a-Cup task from scratch, when only raw sensor streams are used for both controller input and in the auxiliary reward definition.
Data-efficient Hindsight Off-policy Option Learning
We introduce Hindsight Off-policy Options (HO2), a data-efficient option learning algorithm.
Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning
In practice, however, standard off-policy algorithms fail in the batch setting for continuous control.
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots.
Solving math word problems with process- and outcome-based feedback
Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks.
Ranked #30 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
