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Found 19 papers, 15 papers with code
Generalization to New Sequential Decision Making Tasks with In-Context Learning
By training on large diverse offline datasets, our model is able to learn new MiniHack and Procgen tasks without any weight updates from just a handful of demonstrations.
Motif: Intrinsic Motivation from Artificial Intelligence Feedback
Exploring rich environments and evaluating one's actions without prior knowledge is immensely challenging.
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs
This results in an algorithm which sets a new state of the art across 16 tasks from the MiniHack suite used in prior work, and also performs robustly on Habitat and Montezuma's Revenge.
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
For this setting, we develop and study a simple meta-algorithmic pipeline that learns an inverse dynamics model on the labelled data to obtain proxy-labels for the unlabelled data, followed by the use of any offline RL algorithm on the true and proxy-labelled trajectories.
Exploration via Elliptical Episodic Bonuses
In recent years, a number of reinforcement learning (RL) methods have been proposed to explore complex environments which differ across episodes.
Imitation Learning from Pixel Observations for Continuous Control
In this setting, we explore recipes for imitation learning based on adversarial learning and optimal transport.
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
Direct policy gradient methods for reinforcement learning are a successful approach for a variety of reasons: they are model free, they directly optimize the performance metric of interest, and they allow for richly parameterized policies.
Disagreement-Regularized Imitation Learning
We present a simple and effective algorithm designed to address the covariate shift problem in imitation learning.
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
We present an algorithm, HOMER, for exploration and reinforcement learning in rich observation environments that are summarizable by an unknown latent state space.
Explicit Explore-Exploit Algorithms in Continuous State Spaces
We present a new model-based algorithm for reinforcement learning (RL) which consists of explicit exploration and exploitation phases, and is applicable in large or infinite state spaces.
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training.
Prediction Under Uncertainty with Error Encoding Networks
In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty.
Prediction Under Uncertainty with Error-Encoding Networks
In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty.
Model-Based Planning with Discrete and Continuous Actions
Action planning using learned and differentiable forward models of the world is a general approach which has a number of desirable properties, including improved sample complexity over model-free RL methods, reuse of learned models across different tasks, and the ability to perform efficient gradient-based optimization in continuous action spaces.
Tracking the World State with Recurrent Entity Networks
The EntNet sets a new state-of-the-art on the bAbI tasks, and is the first method to solve all the tasks in the 10k training examples setting.
Ranked #5 on
Procedural Text Understanding
on ProPara
Recurrent Orthogonal Networks and Long-Memory Tasks
Although RNNs have been shown to be powerful tools for processing sequential data, finding architectures or optimization strategies that allow them to model very long term dependencies is still an active area of research.
Deep Convolutional Networks on Graph-Structured Data
Deep Learning's recent successes have mostly relied on Convolutional Networks, which exploit fundamental statistical properties of images, sounds and video data: the local stationarity and multi-scale compositional structure, that allows expressing long range interactions in terms of shorter, localized interactions.
The Loss Surfaces of Multilayer Networks
We show that for large-size decoupled networks the lowest critical values of the random loss function form a layered structure and they are located in a well-defined band lower-bounded by the global minimum.
Fast Training of Convolutional Networks through FFTs
Convolutional networks are one of the most widely employed architectures in computer vision and machine learning.
