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Found 11 papers, 6 papers with code
Scaling Instructable Agents Across Many Simulated Worlds
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
Semantic Segmentation on 3D Point Clouds with High Density Variations
LiDAR scanning for surveying applications acquire measurements over wide areas and long distances, which produces large-scale 3D point clouds with significant local density variations.
Solving Reasoning Tasks with a Slot Transformer
The ability to carve the world into useful abstractions in order to reason about time and space is a crucial component of intelligence.
Rapid Task-Solving in Novel Environments
We show that EPNs learn to execute a value iteration-like planning algorithm and that they generalize to situations beyond their training experience.
Generalization of Reinforcement Learners with Working and Episodic Memory
In this paper, we aim to develop a comprehensive methodology to test different kinds of memory in an agent and assess how well the agent can apply what it learns in training to a holdout set that differs from the training set along dimensions that we suggest are relevant for evaluating memory-specific generalization.
OpenSpiel: A Framework for Reinforcement Learning in Games
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Interval timing in deep reinforcement learning agents
The measurement of time is central to intelligent behavior.
Relational recurrent neural networks
Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods.
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Language Modelling
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Relational inductive biases, deep learning, and graph networks
As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
Dyna Planning using a Feature Based Generative Model
Dyna-style reinforcement learning is a powerful approach for problems where not much real data is available.
Grounded Language Learning in a Simulated 3D World
Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions.
