Search Results for author:
Found 27 papers, 18 papers with code
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
We propose a novel model-based algorithm, Behavior-Regularized Model-ENsemble (BREMEN) that can effectively optimize a policy offline using 10-20 times fewer data than prior works.
Dynamics-Aware Unsupervised Skill Discovery
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment.
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning
Can we instead develop efficient reinforcement learning methods that acquire diverse skills without any reward function, and then repurpose these skills for downstream tasks?
Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog
This is a critical shortcoming for applying RL to real-world problems where collecting data is expensive, and models must be tested offline before being deployed to interact with the environment -- e. g. systems that learn from human interaction.
A Divergence Minimization Perspective on Imitation Learning Methods
We present $f$-MAX, an $f$-divergence generalization of AIRL [Fu et al., 2018], a state-of-the-art IRL method.
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
Hierarchical reinforcement learning has demonstrated significant success at solving difficult reinforcement learning (RL) tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real
Our method hinges on the use of hierarchical sim2real -- a simulated environment is used to learn low-level goal-reaching skills, which are then used as the action space for a high-level RL controller, also trained in simulation.
Dynamics-Aware Unsupervised Discovery of Skills
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment.
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment.
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
We find that, using our approach, agents can learn to solve to diverse, temporally-extended tasks such as object sorting and multi-object rearrangement, including from raw pixel observations.
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
Burda et al. (2015) introduced a multi-sample variational bound, IWAE, that is at least as tight as the standard variational lower bound and becomes increasingly tight as the number of samples increases.
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.
Data-Efficient Hierarchical Reinforcement Learning
In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
The Mirage of Action-Dependent Baselines in Reinforcement Learning
Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance.
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
TDMs combine the benefits of model-free and model-based RL: they leverage the rich information in state transitions to learn very efficiently, while still attaining asymptotic performance that exceeds that of direct model-based RL methods.
TRL: Discriminative Hints for Scalable Reverse Curriculum Learning
However, tasks with sparse rewards remain challenging when the state space is large.
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
In this work, we propose an autonomous method for safe and efficient reinforcement learning that simultaneously learns a forward and reset policy, with the reset policy resetting the environment for a subsequent attempt.
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
Off-policy model-free deep reinforcement learning methods using previously collected data can improve sample efficiency over on-policy policy gradient techniques.
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
This paper proposes a general method for improving the structure and quality of sequences generated by a recurrent neural network (RNN), while maintaining information originally learned from data, as well as sample diversity.
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
We analyze the connection between Q-Prop and existing model-free algorithms, and use control variate theory to derive two variants of Q-Prop with conservative and aggressive adaptation.
Categorical Reparameterization with Gumbel-Softmax
Categorical variables are a natural choice for representing discrete structure in the world.
Categorical Reparametrization with Gumbel-Softmax
Categorical variables are a natural choice for representing discrete structure in the world.
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates
In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on off-policy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots.
Continuous Deep Q-Learning with Model-based Acceleration
In this paper, we explore algorithms and representations to reduce the sample complexity of deep reinforcement learning for continuous control tasks.
MuProp: Unbiased Backpropagation for Stochastic Neural Networks
Deep neural networks are powerful parametric models that can be trained efficiently using the backpropagation algorithm.
Neural Adaptive Sequential Monte Carlo
Experiments indicate that NASMC significantly improves inference in a non-linear state space model outperforming adaptive proposal methods including the Extended Kalman and Unscented Particle Filters.
Towards Deep Neural Network Architectures Robust to Adversarial Examples
We perform various experiments to assess the removability of adversarial examples by corrupting with additional noise and pre-processing with denoising autoencoders (DAEs).
