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Found 25 papers, 6 papers with code
Neuro-Symbolic Approaches for Text-Based Policy Learning
We outline a method for end-to-end differentiable symbolic rule learning and show that such symbolic policies outperform previous state-of-the-art methods in text-based RL for the coin collector environment from 5-10x fewer training games.
Self-Refinement of Language Models from External Proxy Metrics Feedback
It is often desirable for Large Language Models (LLMs) to capture multiple objectives when providing a response.
API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs
There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks.
BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback
Following the success of Proximal Policy Optimization (PPO) for Reinforcement Learning from Human Feedback (RLHF), new techniques such as Sequence Likelihood Calibration (SLiC) and Direct Policy Optimization (DPO) have been proposed that are offline in nature and use rewards in an indirect manner.
Using Large Language Models to Automate Category and Trend Analysis of Scientific Articles: An Application in Ophthalmology
Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines.
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning
Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games.
LOA: Logical Optimal Actions for Text-based Interaction Games
We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games.
Neuro-Symbolic Reinforcement Learning with First-Order Logic
Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided.
Reinforcement Learning with External Knowledge by using Logical Neural Networks
Specifically, we propose an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained framework such as action shielding and guide.
Ensemble of Discriminators for Domain Adaptation in Multiple Sound Source 2D Localization
This paper introduces an ensemble of discriminators that improves the accuracy of a domain adaptation technique for the localization of multiple sound sources.
Data-Efficient Framework for Real-world Multiple Sound Source 2D Localization
Moreover, learning for different microphone array layouts makes the task more complicated due to the infinite number of possible layouts.
Learning Multiple Sound Source 2D Localization
In this paper, we propose novel deep learning based algorithms for multiple sound source localization.
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games
Our bootstrapped agent shows improved generalization in solving unseen TextWorld games, using 10x-20x fewer training games compared to previous state-of-the-art methods despite requiring less number of training episodes.
Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos
In this paper, we study the case of event detection in sports videos for unstructured environments with arbitrary camera angles.
Design and Implementation of Linked Planning Domain Definition Language
Planning is a critical component of any artificial intelligence system that concerns the realization of strategies or action sequences typically for intelligent agents and autonomous robots.
Spatially-weighted Anomaly Detection with Regression Model
Visual anomaly detection is common in several applications including medical screening and production quality check.
Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning
Experimental results show that the proposed adversarial learning method from raw videos produces a similar performance to state-of-the-art imitation learning techniques while frequently outperforming existing hand-crafted video imitation methods.
Constrained Exploration and Recovery from Experience Shaping
We consider the problem of reinforcement learning under safety requirements, in which an agent is trained to complete a given task, typically formalized as the maximization of a reward signal over time, while concurrently avoiding undesirable actions or states, associated to lower rewards, or penalties.
Safe Exploration in Markov Decision Processes with Time-Variant Safety using Spatio-Temporal Gaussian Process
This paper presents a learning algorithm called ST-SafeMDP for exploring Markov decision processes (MDPs) that is based on the assumption that the safety features are a priori unknown and time-variant.
Reinforcement Learning Testbed for Power-Consumption Optimization
Common approaches to control a data-center cooling system rely on approximated system/environment models that are built upon the knowledge of mechanical cooling and electrical and thermal management.
Systems and Control
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention
This paper is a contribution towards interpretability of the deep learning models in different applications of time-series.
Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning
In the results section we demonstrate the features of the algorithm using MNIST handwritten digit dataset and latter apply the technique to a real-world obstacle detection problem.
Deep Reinforcement Learning for High Precision Assembly Tasks
High precision assembly of mechanical parts requires accuracy exceeding the robot precision.
Conditional generation of multi-modal data using constrained embedding space mapping
We present a conditional generative model that maps low-dimensional embeddings of multiple modalities of data to a common latent space hence extracting semantic relationships between them.
