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Found 13 papers, 7 papers with code
Routing Enforced Generative Model for Recipe Generation
One of the most challenging part of recipe generation is to deal with the complex restrictions among the input ingredients.
Homophonic Pun Generation with Lexically Constrained Rewriting
Punning is a creative way to make conversation enjoyable and literary writing elegant.
Towards Control-Centric Representations in Reinforcement Learning from Images
Image-based Reinforcement Learning is a practical yet challenging task.
Representation Learning in Deep RL via Discrete Information Bottleneck
Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations.
Behavior Prior Representation learning for Offline Reinforcement Learning
Our method, Behavior Prior Representation (BPR), learns state representations with an easy-to-integrate objective based on behavior cloning of the dataset: we first learn a state representation by mimicking actions from the dataset, and then train a policy on top of the fixed representation, using any off-the-shelf Offline RL algorithm.
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that are capable of solving multiple tasks and reach a diverse set of objectives.
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
We find that contemporary representation learning techniques can fail on datasets where the noise is a complex and time dependent process, which is prevalent in practical applications.
SimSR: Simple Distance-based State Representation for Deep Reinforcement Learning
This work explores how to learn robust and generalizable state representation from image-based observations with deep reinforcement learning methods.
TEAC: Intergrating Trust Region and Max Entropy Actor Critic for Continuous Control
Trust region methods and maximum entropy methods are two state-of-the-art branches used in reinforcement learning (RL) for the benefits of stability and exploration in continuous environments, respectively.
Automated Chess Commentator Powered by Neural Chess Engine
In this paper, we explore a new approach for automated chess commentary generation, which aims to generate chess commentary texts in different categories (e. g., description, comparison, planning, etc.).
Massive Styles Transfer with Limited Labeled Data
In this paper, we propose a multi-agent style transfer system (MAST) for addressing multiple style transfer tasks with limited labeled data, by leveraging abundant unlabeled data and the mutual benefit among the multiple styles.
A Semi-Supervised Approach for Low-Resourced Text Generation
The low-resource (of labeled data) problem is quite common in different task generation tasks, but unlabeled data are usually abundant.
Towards Automatic Generation of Product Reviews from Aspect-Sentiment Scores
Data-to-text generation is very essential and important in machine writing applications.
