Search Results for author:
Found 348 papers, 188 papers with code
On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient
Likelihood ratio policy gradient methods have been some of the most successful reinforcement learning algorithms, especially for learning on physical systems.
Safe Exploration in Markov Decision Processes
We show that imposing safety by restricting attention to the resulting set of guaranteed safe policies is NP-hard.
Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics
We present a policy search method that uses iteratively refitted local linear models to optimize trajectory distributions for large, continuous problems.
Learning Contact-Rich Manipulation Skills with Guided Policy Search
Autonomous learning of object manipulation skills can enable robots to acquire rich behavioral repertoires that scale to the variety of objects found in the real world.
Robotics
Trust Region Policy Optimization
We describe an iterative procedure for optimizing policies, with guaranteed monotonic improvement.
End-to-End Training of Deep Visuomotor Policies
Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control.
High-Dimensional Continuous Control Using Generalized Advantage Estimation
Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks.
Combinatorial Energy Learning for Image Segmentation
We introduce a new machine learning approach for image segmentation that uses a neural network to model the conditional energy of a segmentation given an image.
Gradient Estimation Using Stochastic Computation Graphs
In a variety of problems originating in supervised, unsupervised, and reinforcement learning, the loss function is defined by an expectation over a collection of random variables, which might be part of a probabilistic model or the external world.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
By parameterizing our learned model with a neural network, we are able to develop a scalable and efficient approach to exploration bonuses that can be applied to tasks with complex, high-dimensional state spaces.
Ranked #24 on
Atari Games
on Atari 2600 Q*Bert
Learning Deep Neural Network Policies with Continuous Memory States
We evaluate our method on tasks involving continuous control in manipulation and navigation settings, and show that our method can learn complex policies that successfully complete a range of tasks that require memory.
Deep Spatial Autoencoders for Visuomotor Learning
Our method uses a deep spatial autoencoder to acquire a set of feature points that describe the environment for the current task, such as the positions of objects, and then learns a motion skill with these feature points using an efficient reinforcement learning method based on local linear models.
Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search
We propose to combine MPC with reinforcement learning in the framework of guided policy search, where MPC is used to generate data at training time, under full state observations provided by an instrumented training environment.
Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration
In this paper, we present a robotic model-based reinforcement learning method that combines ideas from model identification and model predictive control.
Model-based Reinforcement Learning
Model Predictive Control
+2
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors
One of the key challenges in applying reinforcement learning to complex robotic control tasks is the need to gather large amounts of experience in order to find an effective policy for the task at hand.
Model-based Reinforcement Learning
Model Predictive Control
+3
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints
We propose a novel, more powerful combination of both distribution and pairwise image alignment, and remove the requirement for expensive annotation by using weakly aligned pairs of images in the source and target domains.
Inverse Reinforcement Learning via Deep Gaussian Process
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations.
Value Iteration Networks
We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within.
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
We explore how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high-dimensional robotic systems.
PLATO: Policy Learning using Adaptive Trajectory Optimization
PLATO also maintains the MPC cost as an objective to avoid highly undesirable actions that would result from strictly following the learned policy before it has been fully trained.
Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration
In this paper, we describe an approach to learning from demonstration that can be used to train soft robotic hands to perform dexterous manipulation tasks.
Benchmarking Deep Reinforcement Learning for Continuous Control
Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning.
Ranked #1 on
Continuous Control
on Inverted Pendulum
Backprop KF: Learning Discriminative Deterministic State Estimators
We show that this procedure can be used to train state estimators that use complex input, such as raw camera images, which must be processed using expressive nonlinear function approximators such as convolutional neural networks.
VIME: Variational Information Maximizing Exploration
While there are methods with optimality guarantees in the setting of discrete state and action spaces, these methods cannot be applied in high-dimensional deep RL scenarios.
Cooperative Inverse Reinforcement Learning
For an autonomous system to be helpful to humans and to pose no unwarranted risks, it needs to align its values with those of the humans in its environment in such a way that its actions contribute to the maximization of value for the humans.
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner.
Ranked #3 on
Image Generation
on Stanford Cars
Learning to Poke by Poking: Experiential Learning of Intuitive Physics
We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics.
Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer
Using deep reinforcement learning to train general purpose neural network policies alleviates some of the burden of manual representation engineering by using expressive policy classes, but exacerbates the challenge of data collection, since such methods tend to be less efficient than RL with low-dimensional, hand-designed representations.
Deep Reinforcement Learning for Tensegrity Robot Locomotion
We evaluate our method with real-world and simulated experiments on the SUPERball tensegrity robot, showing that the learned policies generalize to changes in system parameters, unreliable sensor measurements, and variation in environmental conditions, including varied terrains and a range of different gravities.
Learning from the Hindsight Plan -- Episodic MPC Improvement
To bring the next real-world execution closer to the hindsight plan, our approach learns to re-shape the original cost function with the goal of satisfying the following property: short horizon planning (as realistic during real executions) with respect to the shaped cost should result in mimicking the hindsight plan.
Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States
Autonomous learning of robotic skills can allow general-purpose robots to learn wide behavioral repertoires without requiring extensive manual engineering.
Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model
Nevertheless, often the overall gist of what the policy does in simulation remains valid in the real world.
Variational Lossy Autoencoder
Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification.
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
The activations of the RNN store the state of the "fast" RL algorithm on the current (previously unseen) MDP.
A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models
In particular, we demonstrate an equivalence between a sample-based algorithm for maximum entropy IRL and a GAN in which the generator's density can be evaluated and is provided as an additional input to the discriminator.
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks.
Ranked #1 on
Atari Games
on Atari 2600 Freeway
The Off-Switch Game
We analyze a simple game between a human H and a robot R, where H can press R's off switch but R can disable the off switch.
Generalizing Skills with Semi-Supervised Reinforcement Learning
We evaluate our method on challenging tasks that require control directly from images, and show that our approach can improve the generalization of a learned deep neural network policy by using experience for which no reward function is available.
A K-fold Method for Baseline Estimation in Policy Gradient Algorithms
The high variance issue in unbiased policy-gradient methods such as VPG and REINFORCE is typically mitigated by adding a baseline.
Uncertainty-Aware Reinforcement Learning for Collision Avoidance
However, practical deployment of reinforcement learning methods must contend with the fact that the training process itself can be unsafe for the robot.
Adversarial Attacks on Neural Network Policies
Machine learning classifiers are known to be vulnerable to inputs maliciously constructed by adversaries to force misclassification.
Enabling Robots to Communicate their Objectives
We show that certain approximate-inference models lead to the robot generating example behaviors that better enable users to anticipate what it will do in novel situations.
Reinforcement Learning with Deep Energy-Based Policies
We propose a method for learning expressive energy-based policies for continuous states and actions, which has been feasible only in tabular domains before.
Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation
Manipulation of deformable objects, such as ropes and cloth, is an important but challenging problem in robotics.
Third-Person Imitation Learning
A key difficulty in reinforcement learning is specifying a reward function for the agent to optimize.
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning
People can learn a wide range of tasks from their own experience, but can also learn from observing other creatures.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.
Prediction and Control with Temporal Segment Models
We introduce a method for learning the dynamics of complex nonlinear systems based on deep generative models over temporal segments of states and actions.
Emergence of Grounded Compositional Language in Multi-Agent Populations
By capturing statistical patterns in large corpora, machine learning has enabled significant advances in natural language processing, including in machine translation, question answering, and sentiment analysis.
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
Bridging the 'reality gap' that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability.
One-Shot Imitation Learning
A neural net is trained that takes as input one demonstration and the current state (which initially is the initial state of the other demonstration of the pair), and outputs an action with the goal that the resulting sequence of states and actions matches as closely as possible with the second demonstration.
Learning Visual Servoing with Deep Features and Fitted Q-Iteration
Our approach is based on servoing the camera in the space of learned visual features, rather than image pixels or manually-designed keypoints.
Stochastic Neural Networks for Hierarchical Reinforcement Learning
Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Equivalence Between Policy Gradients and Soft Q-Learning
A partial explanation may be that $Q$-learning methods are secretly implementing policy gradient updates: we show that there is a precise equivalence between $Q$-learning and policy gradient methods in the setting of entropy-regularized reinforcement learning, that "soft" (entropy-regularized) $Q$-learning is exactly equivalent to a policy gradient method.
Probabilistically Safe Policy Transfer
Although learning-based methods have great potential for robotics, one concern is that a robot that updates its parameters might cause large amounts of damage before it learns the optimal policy.
Automatic Goal Generation for Reinforcement Learning Agents
Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing.
Constrained Policy Optimization
For many applications of reinforcement learning it can be more convenient to specify both a reward function and constraints, rather than trying to design behavior through the reward function.
UCB Exploration via Q-Ensembles
We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning.
Parameter Space Noise for Exploration
Combining parameter noise with traditional RL methods allows to combine the best of both worlds.
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
We explore deep reinforcement learning methods for multi-agent domains.
Ranked #1 on
SMAC+
on Def_Infantry_sequential
Hindsight Experience Replay
Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL).
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation
Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator.
A Simple Neural Attentive Meta-Learner
Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task.
Reverse Curriculum Generation for Reinforcement Learning
The robot is trained in reverse, gradually learning to reach the goal from a set of start states increasingly far from the goal.
Mutual Alignment Transfer Learning
Training robots for operation in the real world is a complex, time consuming and potentially expensive task.
Deep Object-Centric Representations for Generalizable Robot Learning
We devise an object-level attentional mechanism that can be used to determine relevant objects from a few trajectories or demonstrations, and then immediately incorporate those objects into a learned policy.
Learning Generalized Reactive Policies using Deep Neural Networks
We show that a deep neural network can be used to learn and represent a \emph{generalized reactive policy} (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances.
Learning with Opponent-Learning Awareness
We also show that the LOLA update rule can be efficiently calculated using an extension of the policy gradient estimator, making the method suitable for model-free RL.
One-Shot Visual Imitation Learning via Meta-Learning
In this work, we present a meta-imitation learning method that enables a robot to learn how to learn more efficiently, allowing it to acquire new skills from just a single demonstration.
Overcoming Exploration in Reinforcement Learning with Demonstrations
Exploration in environments with sparse rewards has been a persistent problem in reinforcement learning (RL).
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation
To address the need to learn complex policies with few samples, we propose a generalized computation graph that subsumes value-based model-free methods and model-based methods, with specific instantiations interpolating between model-free and model-based.
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence.
Synkhronos: a Multi-GPU Theano Extension for Data Parallelism
We present Synkhronos, an extension to Theano for multi-GPU computations leveraging data parallelism.
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation
Imitation learning is a powerful paradigm for robot skill acquisition.
Domain Randomization and Generative Models for Robotic Grasping
In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis.
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
By randomizing the dynamics of the simulator during training, we are able to develop policies that are capable of adapting to very different dynamics, including ones that differ significantly from the dynamics on which the policies were trained.
Robotics Systems and Control
Asymmetric Actor Critic for Image-Based Robot Learning
While several recent works have shown promising results in transferring policies trained in simulation to the real world, they often do not fully utilize the advantage of working with a simulator.
Meta Learning Shared Hierarchies
We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives---policies that are executed for large numbers of timesteps.
Interpretable and Pedagogical Examples
Teachers intentionally pick the most informative examples to show their students.
Inverse Reward Design
When designing the reward, we might think of some specific training scenarios, and make sure that the reward will lead to the right behavior in those scenarios.
Safer Classification by Synthesis
We posit that a generative approach is the natural remedy for this problem, and propose a method for classification using generative models.
A Berkeley View of Systems Challenges for AI
With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artificial Intelligence) has moved from research labs to production.
PixelSNAIL: An Improved Autoregressive Generative Model
Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio.
Self-Supervised Learning of Object Motion Through Adversarial Video Prediction
In this paper, we study the problem of multi-step video prediction, where the goal is to predict a sequence of future frames conditioned on a short context.
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
A platform for Applied Reinforcement Learning (Applied RL)
Ranked #1 on
Continuous Control
on Lunar Lander (OpenAI Gym)
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
Humans and animals are capable of learning a new behavior by observing others perform the skill just once.
Evolved Policy Gradients
We propose a metalearning approach for learning gradient-based reinforcement learning (RL) algorithms.
Meta-Reinforcement Learning of Structured Exploration Strategies
Exploration is a fundamental challenge in reinforcement learning (RL).
Model-Ensemble Trust-Region Policy Optimization
In this paper, we analyze the behavior of vanilla model-based reinforcement learning methods when deep neural networks are used to learn both the model and the policy, and show that the learned policy tends to exploit regions where insufficient data is available for the model to be learned, causing instability in training.
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
We consider the problem of exploration in meta reinforcement learning.
Accelerated Methods for Deep Reinforcement Learning
Deep reinforcement learning (RL) has achieved many recent successes, yet experiment turn-around time remains a key bottleneck in research and in practice.
Composable Deep Reinforcement Learning for Robotic Manipulation
Second, we show that policies learned with soft Q-learning can be composed to create new policies, and that the optimality of the resulting policy can be bounded in terms of the divergence between the composed policies.
Learning Robotic Assembly from CAD
We propose to leverage this prior knowledge by guiding RL along a geometric motion plan, calculated using the CAD data.
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stochastic policy itself and does not make any additional assumptions about the MDP.
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time.
Universal Planning Networks
We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images.
Stochastic Adversarial Video Prediction
However, learning to predict raw future observations, such as frames in a video, is exceedingly challenging -- the ambiguous nature of the problem can cause a naively designed model to average together possible futures into a single, blurry prediction.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
We further explore a number of methods for integrating multiple clips into the learning process to develop multi-skilled agents capable of performing a rich repertoire of diverse skills.
Latent Space Policies for Hierarchical Reinforcement Learning
In contrast to methods that explicitly restrict or cripple lower layers of a hierarchy to force them to use higher-level modulating signals, each layer in our framework is trained to directly solve the task, but acquires a range of diverse strategies via a maximum entropy reinforcement learning objective.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
The Limits and Potentials of Deep Learning for Robotics
In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning.
Robotics
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
We show that we can learn continuous latent representations of trajectories, which are effective in solving temporally extended and multi-stage problems.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization.
Learning Plannable Representations with Causal InfoGAN
Finally, to generate a visual plan, we project the current and goal observations onto their respective states in the planning model, plan a trajectory, and then use the generative model to transform the trajectory to a sequence of observations.
Variational Option Discovery Algorithms
We explore methods for option discovery based on variational inference and make two algorithmic contributions.
Transfer Learning for Estimating Causal Effects using Neural Networks
We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature.
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
Model-based reinforcement learning (RL) has proven to be a data efficient approach for learning control tasks but is difficult to utilize in domains with complex observations such as images.
Model-based Reinforcement Learning
reinforcement-learning
+1
Model-Based Reinforcement Learning via Meta-Policy Optimization
Finally, we demonstrate that our approach is able to match the asymptotic performance of model-free methods while requiring significantly less experience.
Model-based Reinforcement Learning
reinforcement-learning
+1
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
By enforcing a constraint on the mutual information between the observations and the discriminator's internal representation, we can effectively modulate the discriminator's accuracy and maintain useful and informative gradients.
SFV: Reinforcement Learning of Physical Skills from Videos
In this paper, we propose a method that enables physically simulated characters to learn skills from videos (SFV).
ProMP: Proximal Meta-Policy Search
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.
Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation
We show that a simulated robotic car and a real-world RC car can gather data and train fully autonomously without any human-provided labels beyond those needed to train the detectors, and then at test-time be able to accomplish a variety of different tasks.
Establishing Appropriate Trust via Critical States
In order to effectively interact with or supervise a robot, humans need to have an accurate mental model of its capabilities and how it acts.
Robotics
One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks
We consider the problem of learning multi-stage vision-based tasks on a real robot from a single video of a human performing the task, while leveraging demonstration data of subtasks with other objects.
Modular Architecture for StarCraft II with Deep Reinforcement Learning
We present a novel modular architecture for StarCraft II AI.
An Algorithmic Perspective on Imitation Learning
This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning.
Guiding Policies with Language via Meta-Learning
However, a single instruction may be insufficient to fully communicate our intent or, even if it is, may be insufficient for an autonomous agent to actually understand how to perform the desired task.
The Importance of Sampling inMeta-Reinforcement Learning
Results are presented on a new environment we call `Krazy World': a difficult high-dimensional gridworld which is designed to highlight the importance of correctly differentiating through sampling distributions in meta-reinforcement learning.
Soft Actor-Critic Algorithms and Applications
A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs
In this work, we show how visual trajectories can be hallucinated to appear successful by altering agent observations using a generative model trained on relatively few snapshots of the goal.
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Flow-based generative models are powerful exact likelihood models with efficient sampling and inference.
Ranked #14 on
Image Generation
on ImageNet 32x32
(bpd metric)
Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight
Deep reinforcement learning provides a promising approach for vision-based control of real-world robots.
Preferences Implicit in the State of the World
We find that information from the initial state can be used to infer both side effects that should be avoided as well as preferences for how the environment should be organized.
Domain Randomization for Active Pose Estimation
In this work, we investigate how to improve the accuracy of domain randomization based pose estimation.
Towards Characterizing Divergence in Deep Q-Learning
Deep Q-Learning (DQL), a family of temporal difference algorithms for control, employs three techniques collectively known as the `deadly triad' in reinforcement learning: bootstrapping, off-policy learning, and function approximation.
Guided Meta-Policy Search
Reinforcement learning (RL) algorithms have demonstrated promising results on complex tasks, yet often require impractical numbers of samples since they learn from scratch.
Learning to Reinforcement Learn by Imitation
Meta-reinforcement learning aims to learn fast reinforcement learning (RL) procedures that can be applied to new tasks or environments.
Deep Unsupervised Cardinality Estimation
To produce a truly usable estimator, we develop a Monte Carlo integration scheme on top of autoregressive models that can efficiently handle range queries with dozens of dimensions or more.
Learning Robotic Manipulation through Visual Planning and Acting
We further demonstrate our approach on learning to imagine and execute in 3 environments, the final of which is deformable rope manipulation on a PR2 robot.
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations.
Ranked #5 on
Image Classification
on SVHN
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
The bits-back argument suggests that latent variable models can be turned into lossless compression schemes.
Compression with Flows via Local Bits-Back Coding
Likelihood-based generative models are the backbones of lossless compression due to the guaranteed existence of codes with lengths close to negative log likelihood.
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
In this work, we propose multiplicative compositional policies (MCP), a method for learning reusable motor skills that can be composed to produce a range of complex behaviors.
Learning latent state representation for speeding up exploration
In this work, we take a representation learning viewpoint on exploration, utilizing prior experience to learn effective latent representations, which can subsequently indicate which regions to explore.
Sub-policy Adaptation for Hierarchical Reinforcement Learning
Hierarchical reinforcement learning is a promising approach to tackle long-horizon decision-making problems with sparse rewards.
Goal-conditioned Imitation Learning
Designing rewards for Reinforcement Learning (RL) is challenging because it needs to convey the desired task, be efficient to optimize, and be easy to compute.
Evaluating Protein Transfer Learning with TAPE
Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation techniques.
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
But in the era of deep learning, a natural suggestion researchers make is to avoid mathematical models of human behavior that are fraught with specific assumptions, and instead use a purely data-driven approach.
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
Deep reinforcement learning (RL) algorithms can use high-capacity deep networks to learn directly from image observations.
Benchmarking Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) is widely seen as having the potential to be significantly more sample efficient than model-free RL.
BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks
The discrepancy between post-layout and schematic simulation results continues to widen in analog design due in part to the domination of layout parasitics.
Likelihood Contribution based Multi-scale Architecture for Generative Flows
Deep generative modeling using flows has gained popularity owing to the tractable exact log-likelihood estimation with efficient training and synthesis process.
DoorGym: A Scalable Door Opening Environment And Baseline Agent
We introduce DoorGym, an open-source door opening simulation framework designed to utilize domain randomization to train a stable policy.
rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch
rlpyt is designed as a high-throughput code base for small- to medium-scale research in deep RL.
Dynamical System Embedding for Efficient Intrinsically Motivated Artificial Agents
In this work, we develop a novel approach for the estimation of empowerment in unknown arbitrary dynamics from visual stimulus only, without sampling for the estimation of MIAS.
PatchFormer: A neural architecture for self-supervised representation learning on images
In this paper, we propose a neural architecture for self-supervised representation learning on raw images called the PatchFormer which learns to model spatial dependencies across patches in a raw image.
Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization
We formalize the problem of trading-off DNN training time and memory requirements as the tensor rematerialization optimization problem, a generalization of prior checkpointing strategies.
On the Utility of Learning about Humans for Human-AI Coordination
While we would like agents that can coordinate with humans, current algorithms such as self-play and population-based training create agents that can coordinate with themselves.
Geometry-Aware Neural Rendering
Understanding the 3-dimensional structure of the world is a core challenge in computer vision and robotics.
Asynchronous Methods for Model-Based Reinforcement Learning
In this work, we propose an asynchronous framework for model-based reinforcement learning methods that brings down the run time of these algorithms to be just the data collection time.
Model-based Reinforcement Learning
reinforcement-learning
+1
Learning to Manipulate Deformable Objects without Demonstrations
Second, instead of jointly learning both the pick and the place locations, we only explicitly learn the placing policy conditioned on random pick points.
Plan Arithmetic: Compositional Plan Vectors for Multi-Task Control
We show that CPVs can be learned within a one-shot imitation learning framework without any additional supervision or information about task hierarchy, and enable a demonstration-conditioned policy to generalize to tasks that sequence twice as many skills as the tasks seen during training.
Natural Image Manipulation for Autoregressive Models Using Fisher Scores
Deep autoregressive models are one of the most powerful models that exist today which achieve state-of-the-art bits per dim.
Compositional Plan Vectors
We show that CPVs can be learned within a one-shot imitation learning framework without any additional supervision or information about task hierarchy, and enable a demonstration-conditioned policy to generalize to tasks that sequence twice as many skills as the tasks seen during training.
Adaptive Online Planning for Continual Lifelong Learning
We study learning control in an online reset-free lifelong learning scenario, where mistakes can compound catastrophically into the future and the underlying dynamics of the environment may change.
Learning Efficient Representation for Intrinsic Motivation
The core idea is to represent the relation between action sequences and future states using a stochastic dynamic model in latent space with a specific form.
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos
In this paper, we study how these challenges can be alleviated with an automated robotic learning framework, in which multi-stage tasks are defined simply by providing videos of a human demonstrator and then learned autonomously by the robot from raw image observations.
Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards
While recent progress in deep reinforcement learning has enabled robots to learn complex behaviors, tasks with long horizons and sparse rewards remain an ongoing challenge.
Hierarchical Variational Imitation Learning of Control Programs
Autonomous agents can learn by imitating teacher demonstrations of the intended behavior.
Preventing Imitation Learning with Adversarial Policy Ensembles
To our knowledge, this is the first work regarding the protection of policies in Reinforcement Learning.
Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning
In reinforcement learning, an agent learns to reach a set of goals by means of an external reward signal.
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System
Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free paths towards a desired goal.
GACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfaction
In this work we present a new method of black-box optimization and constraint satisfaction.
Generalized Hindsight for Reinforcement Learning
Compared to standard relabeling techniques, Generalized Hindsight provides a substantially more efficient reuse of samples, which we empirically demonstrate on a suite of multi-task navigation and manipulation tasks.
Hallucinative Topological Memory for Zero-Shot Visual Planning
In visual planning (VP), an agent learns to plan goal-directed behavior from observations of a dynamical system obtained offline, e. g., images obtained from self-supervised robot interaction.
Hierarchically Decoupled Imitation for Morphological Transfer
Learning long-range behaviors on complex high-dimensional agents is a fundamental problem in robot learning.
Learning Predictive Representations for Deformable Objects Using Contrastive Estimation
Using visual model-based learning for deformable object manipulation is challenging due to difficulties in learning plannable visual representations along with complex dynamic models.
Sparse Graphical Memory for Robust Planning
To operate effectively in the real world, agents should be able to act from high-dimensional raw sensory input such as images and achieve diverse goals across long time-horizons.
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
On the DeepMind Control Suite, CURL is the first image-based algorithm to nearly match the sample-efficiency of methods that use state-based features.
Ranked #1 on
Continuous Control
on Finger, spin (DMControl500k)
Reinforcement Learning with Augmented Data
To this end, we present Reinforcement Learning with Augmented Data (RAD), a simple plug-and-play module that can enhance most RL algorithms.
Plan2Vec: Unsupervised Representation Learning by Latent Plans
In this paper we introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning.
Planning to Explore via Self-Supervised World Models
Reinforcement learning allows solving complex tasks, however, the learning tends to be task-specific and the sample efficiency remains a challenge.
Model-Augmented Actor-Critic: Backpropagating through Paths
Current model-based reinforcement learning approaches use the model simply as a learned black-box simulator to augment the data for policy optimization or value function learning.
Mutual Information Maximization for Robust Plannable Representations
The later, while they present low sample complexity, they learn latent spaces that need to reconstruct every single detail of the scene.
Automatic Curriculum Learning through Value Disagreement
Our key insight is that if we can sample goals at the frontier of the set of goals that an agent is able to reach, it will provide a significantly stronger learning signal compared to randomly sampled goals.
Denoising Diffusion Probabilistic Models
We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.
Ranked #2 on
Image Generation
on LSUN Bedroom
Locally Masked Convolution for Autoregressive Models
For tasks such as image completion, these models are unable to use much of the observed context.
Ranked #1 on
Image Generation
on MNIST
AvE: Assistance via Empowerment
One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s).
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods
Lagrangian methods are widely used algorithms for constrained optimization problems, but their learning dynamics exhibit oscillations and overshoot which, when applied to safe reinforcement learning, leads to constraint-violating behavior during agent training.
Self-Supervised Policy Adaptation during Deployment
A natural solution would be to keep training after deployment in the new environment, but this cannot be done if the new environment offers no reward signal.
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Off-policy deep reinforcement learning (RL) has been successful in a range of challenging domains.
Contrastive Code Representation Learning
Recent work learns contextual representations of source code by reconstructing tokens from their context.
Ranked #1 on
Method name prediction
on CodeSearchNet
Variable Skipping for Autoregressive Range Density Estimation
In this paper, we explore a technique, variable skipping, for accelerating range density estimation over deep autoregressive models.
Efficient Empowerment Estimation for Unsupervised Stabilization
We demonstrate our solution for sample-based unsupervised stabilization on different dynamical control systems and show the advantages of our method by comparing it to the existing VLB approaches.
Hybrid Discriminative-Generative Training via Contrastive Learning
In this paper we show that through the perspective of hybrid discriminative-generative training of energy-based models we can make a direct connection between contrastive learning and supervised learning.
Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning
Despite the significant progress of deep reinforcement learning (RL) in solving sequential decision making problems, RL agents often overfit to training environments and struggle to adapt to new, unseen environments.
Robust Reinforcement Learning using Adversarial Populations
Reinforcement Learning (RL) is an effective tool for controller design but can struggle with issues of robustness, failing catastrophically when the underlying system dynamics are perturbed.
Out-of-Distribution Generalization
reinforcement-learning
+1
Visual Imitation Made Easy
We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector.
Decoupling Representation Learning from Reinforcement Learning
In an effort to overcome limitations of reward-driven feature learning in deep reinforcement learning (RL) from images, we propose decoupling representation learning from policy learning.
LaND: Learning to Navigate from Disengagements
However, we believe that these disengagements not only show where the system fails, which is useful for troubleshooting, but also provide a direct learning signal by which the robot can learn to navigate.
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Model-based reinforcement learning (RL) has shown great potential in various control tasks in terms of both sample-efficiency and final performance.
Parallel Training of Deep Networks with Local Updates
Deep learning models trained on large data sets have been widely successful in both vision and language domains.
Reset-Free Lifelong Learning with Skill-Space Planning
We propose Lifelong Skill Planning (LiSP), an algorithmic framework for non-episodic lifelong RL based on planning in an abstract space of higher-order skills.
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation
We present Contrastive Pre-training and Data Augmentation for Efficient Robotic Learning (CoDER), a method that utilizes data augmentation and unsupervised learning to achieve sample-efficient training of real-robot arm policies from sparse rewards.
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay
In this paper, we present Latent Vector Experience Replay (LeVER), a simple modification of existing off-policy RL methods, to address these computational and memory requirements without sacrificing the performance of RL agents.
VideoGen: Generative Modeling of Videos using VQ-VAE and Transformers
We present VideoGen: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos.
Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets
As it turns out, fine-tuning offline RL agents is a non-trivial challenge, due to distribution shift – the agent encounters out-of-distribution samples during online interaction, which may cause bootstrapping error in Q-learning and instability during fine-tuning.
Unsupervised Active Pre-Training for Reinforcement Learning
On DMControl suite, APT beats all baselines in terms of asymptotic performance and data efficiency and dramatically improves performance on tasks that are extremely difficult for training from scratch.
Discrete Predictive Representation for Long-horizon Planning
Discrete representations have been key in enabling robots to plan at more abstract levels and solve temporally-extended tasks more efficiently for decades.
Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates
Furthermore, since our weighted Bellman backups rely on maintaining an ensemble, we investigate how weighted Bellman backups interact with other benefits previously derived from ensembles: (a) Bootstrap; (b) UCB Exploration.
Benefits of Assistance over Reward Learning
By merging reward learning and control, assistive agents can reason about the impact of control actions on reward learning, leading to several advantages over agents based on reward learning.
