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Found 9 papers, 5 papers with code
Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort
In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging.
Learning to Perceive in Deep Model-Free Reinforcement Learning
We investigate whether a model with these characteristics is capable of achieving similar performance to state-of-the-art model-free RL agents that access the full input observation.
The Impact of Data Distribution on Q-learning with Function Approximation
We provide a unified theoretical and empirical analysis as to how different properties of the data distribution influence the performance of Q-learning-based algorithms.
L2F/INESC-ID at SemEval-2019 Task 2: Unsupervised Lexical Semantic Frame Induction using Contextualized Word Representations
We approach all the subtasks by applying a graph clustering algorithm on contextualized embedding representations of the verbs and arguments.
A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers
Our methodology addresses the lack of standardization in the literature that renders the comparison of approaches in different works meaningless, due to differences in metrics, environments, and even experimental design and methodology.
Assisting Unknown Teammates in Unknown Tasks: Ad Hoc Teamwork under Partial Observability
In this paper, we present a novel Bayesian online prediction algorithm for the problem setting of ad hoc teamwork under partial observability (ATPO), which enables on-the-fly collaboration with unknown teammates performing an unknown task without needing a pre-coordination protocol.
Onception: Active Learning with Expert Advice for Real World Machine Translation
Moreover, since we not know in advance which query strategy will be the most adequate for a certain language pair and set of Machine Translation models, we propose to dynamically combine multiple strategies using prediction with expert advice.
Perceive, Represent, Generate: Translating Multimodal Information to Robotic Motion Trajectories
We present Perceive-Represent-Generate (PRG), a novel three-stage framework that maps perceptual information of different modalities (e. g., visual or sound), corresponding to a sequence of instructions, to an adequate sequence of movements to be executed by a robot.
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning
We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
