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Found 21 papers, 18 papers with code
Watch and Match: Supercharging Imitation with Regularized Optimal Transport
Our experiments on 20 visual control tasks across the DeepMind Control Suite, the OpenAI Robotics Suite, and the Meta-World Benchmark demonstrate an average of 7. 8X faster imitation to reach 90% of expert performance compared to prior state-of-the-art methods.
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery
We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsupervised skill discovery that maximizes the mutual information between state-transitions and latent skill vectors.
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning
In this work, we propose Exploratory data for Offline RL (ExORL), a data-centric approach to offline RL.
Automatic Data Augmentation for Generalization in Reinforcement Learning
Deep reinforcement learning (RL) agents often fail to generalize beyond their training environments.
URLB: Unsupervised Reinforcement Learning Benchmark
Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks.
Imitation Learning from Pixel Observations for Continuous Control
In this setting, we explore recipes for imitation learning based on adversarial learning and optimal transport.
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control.
Reinforcement Learning with Prototypical Representations
Unfortunately, in RL, representation learning is confounded with the exploratory experience of the agent -- learning a useful representation requires diverse data, while effective exploration is only possible with coherent representations.
On the model-based stochastic value gradient for continuous reinforcement learning
For over a decade, model-based reinforcement learning has been seen as a way to leverage control-based domain knowledge to improve the sample-efficiency of reinforcement learning agents.
Automatic Data Augmentation for Generalization in Deep Reinforcement Learning
Our agent outperforms other baselines specifically designed to improve generalization in RL.
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without the need for auxiliary losses or pre-training.
Ranked #1 on
Continuous Control
on DeepMind Walker Walk (Images)
On the adequacy of untuned warmup for adaptive optimization
The stability of such algorithms is often improved with a warmup schedule for the learning rate.
Ranked #6 on
Machine Translation
on WMT2016 English-German
Generalized Inner Loop Meta-Learning
Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem.
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
A promising approach is to learn a latent representation together with the control policy.
The Differentiable Cross-Entropy Method
We study the cross-entropy method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant that enables us to differentiate the output of CEM with respect to the objective function's parameters.
Hierarchical Decision Making by Generating and Following Natural Language Instructions
We explore using latent natural language instructions as an expressive and compositional representation of complex actions for hierarchical decision making.
Quasi-hyperbolic momentum and Adam for deep learning
Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning.
Hierarchical Text Generation and Planning for Strategic Dialogue
End-to-end models for goal-orientated dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors.
Deal or No Deal? End-to-End Learning of Negotiation Dialogues
Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions.
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions.
Convolutional Sequence to Sequence Learning
The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks.
