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Found 15 papers, 4 papers with code
Vision-Language Models as a Source of Rewards
Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning.
STEVE-1: A Generative Model for Text-to-Behavior in Minecraft
Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks.
Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models
To accomplish this, we introduce Data-driven Instruction Augmentation for Language-conditioned control (DIAL): we utilize semi-supervised language labels leveraging the semantic understanding of CLIP to propagate knowledge onto large datasets of unlabelled demonstration data and then train language-conditioned policies on the augmented datasets.
Large Language Models Are Human-Level Prompt Engineers
By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers.
Inner Monologue: Embodied Reasoning through Planning with Language Models
We investigate a variety of sources of feedback, such as success detection, scene description, and human interaction.
Learning Domain Invariant Representations in Goal-conditioned Block MDPs
These changes are often spurious and unrelated to the underlying problem, such as background shifts for visual input agents.
Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels
MGLM is a generative joint distribution model over channels.
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks?
An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality
When defining distances, the triangle inequality has proven to be a useful constraint, both theoretically--to prove convergence and optimality guarantees--and empirically--as an inductive bias.
Multichannel Generative Language Models
For conditional generation, the model is given a fully observed channel, and generates the k-1 channels in parallel.
ACTRCE: Augmenting Experience via Teacher’s Advice
Sparse reward is one of the most challenging problems in reinforcement learning (RL).
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise
We demonstrate that the learning performance of our method is more accurately captured by the structure of the covariance matrix of the noise rather than by the variance of gradients.
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning
We first analyze the differences among goal representation, and show that ACTRCE can efficiently solve difficult reinforcement learning problems in challenging 3D navigation tasks, whereas HER with non-language goal representation failed to learn.
Exploring Curvature Noise in Large-Batch Stochastic Optimization
Unfortunately, a major drawback is the so-called generalization gap: large-batch training typically leads to a degradation in generalization performance of the model as compared to small-batch training.
Are You Sure You Want To Do That? Classification with Verification
Classification systems typically act in isolation, meaning they are required to implicitly memorize the characteristics of all candidate classes in order to classify.
