Search Results for author:
Found 16 papers, 8 papers with code
Autonomous Reinforcement Learning: Formalism and Benchmarking
In this paper, we aim to address this discrepancy by laying out a framework for Autonomous Reinforcement Learning (ARL): reinforcement learning where the agent not only learns through its own experience, but also contends with lack of human supervision to reset between trials.
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention
This work shows the ability to learn dexterous manipulation behaviors in the real world with RL without any human intervention.
Continual Learning of Control Primitives : Skill Discovery via Reset-Games
First, in real world settings, when an agent attempts a tasks and fails, the environment must somehow "reset" so that the agent can attempt the task again.
Continual Learning of Control Primitives: Skill Discovery via Reset-Games
Reinforcement learning has the potential to automate the acquisition of behavior in complex settings, but in order for it to be successfully deployed, a number of practical challenges must be addressed.
Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning
A significant challenge for the practical application of reinforcement learning toreal world problems is the need to specify an oracle reward function that correctly defines a task.
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Few-shot classification refers to learning a classifier for new classes given only a few examples.
Ranked #7 on
Few-Shot Image Classification
on Meta-Dataset Rank
Probabilistic Model-Agnostic Meta-Learning
However, a critical challenge in few-shot learning is task ambiguity: even when a powerful prior can be meta-learned from a large number of prior tasks, a small dataset for a new task can simply be too ambiguous to acquire a single model (e. g., a classifier) for that task that is accurate.
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning
A significant challenge for the practical application of reinforcement learning in the real world is the need to specify an oracle reward function that correctly defines a task.
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control
When evaluated on a number of continuous control tasks, Trust-PCL improves the solution quality and sample efficiency of TRPO.
Bridging the Gap Between Value and Policy Based Reinforcement Learning
We establish a new connection between value and policy based reinforcement learning (RL) based on a relationship between softmax temporal value consistency and policy optimality under entropy regularization.
Unsupervised Perceptual Rewards for Imitation Learning
We present a method that is able to identify key intermediate steps of a task from only a handful of demonstration sequences, and automatically identify the most discriminative features for identifying these steps.
An Actor-Critic Algorithm for Sequence Prediction
We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL).
Ranked #8 on
Machine Translation
on IWSLT2015 English-German
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
A Controller-Recognizer Framework: How necessary is recognition for control?
Recently there has been growing interest in building active visual object recognizers, as opposed to the usual passive recognizers which classifies a given static image into a predefined set of object categories.
On Using Monolingual Corpora in Neural Machine Translation
Recent work on end-to-end neural network-based architectures for machine translation has shown promising results for En-Fr and En-De translation.
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images.
