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Found 10 papers, 3 papers with code
Agent-Centric Representations for Multi-Agent Reinforcement Learning
Empirically, agent-centric representation learning leads to the emergence of more complex cooperation strategies between agents as well as enhanced sample efficiency and generalization.
GEM: Group Enhanced Model for Learning Dynamical Control Systems
In this work, we take advantage of these structures to build effective dynamical models that are amenable to sample-based learning.
Reinforcement Learning with Latent Flow
Temporal information is essential to learning effective policies with Reinforcement Learning (RL).
Learning World Graph Decompositions To Accelerate Reinforcement Learning
Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents.
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning
We perform a thorough ablation study to evaluate our approach on a suite of challenging maze tasks, demonstrating significant advantages from the proposed framework over baselines that lack world graph knowledge in terms of performance and efficiency.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Unsupervised Domain Adaptation for Distance Metric Learning
Unsupervised domain adaptation is a promising avenue to enhance the performance of deep neural networks on a target domain, using labels only from a source domain.
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
In addition, our platform is flexible in terms of environment-agent communication topologies, choices of RL methods, changes in game parameters, and can host existing C/C++-based game environments like Arcade Learning Environment.
Channel-Recurrent Autoencoding for Image Modeling
Despite recent successes in synthesizing faces and bedrooms, existing generative models struggle to capture more complex image types, potentially due to the oversimplification of their latent space constructions.
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
Recently, convolutional neural networks (CNNs) have been used as a powerful tool to solve many problems of machine learning and computer vision.
Improved Multimodal Deep Learning with Variation of Information
Deep learning has been successfully applied to multimodal representation learning problems, with a common strategy to learning joint representations that are shared across multiple modalities on top of layers of modality-specific networks.
