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Found 305 papers, 166 papers with code
Inferring Reward Functions from Demonstrators with Unknown Biases
Our goal is to infer reward functions from demonstrations.
CURL: Contrastive Unsupervised Representation Learning for Reinforcement Learning
CURL extracts high level features from raw pixels using a contrastive learning objective and performs off-policy control on top of the extracted features.
Planning to Explore via Latent Disagreement
To solve complex tasks, intelligent agents first need to explore their environments.
Responsive Safety in Reinforcement Learning
This intuition leads to our introduction of PID control for the Lagrange multiplier in constrained RL, which we cast as a dynamical system.
Hierarchically Decoupled Morphological Transfer
Learning long-range behaviors on complex high-dimensional agents is a fundamental problem in robot learning.
Foundation Models for Decision Making: Problems, Methods, and Opportunities
In response to these developments, new paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning.
Preference Transformer: Modeling Human Preferences using Transformers for RL
In this paper, we present Preference Transformer, a neural architecture that models human preferences using transformers.
Aligning Text-to-Image Models using Human Feedback
Our results demonstrate the potential for learning from human feedback to significantly improve text-to-image models.
Robust and Versatile Bipedal Jumping Control through Multi-Task Reinforcement Learning
Such robustness in the proposed multi-task policy enables Cassie to succeed in completing a variety of challenging jump tasks in the real world, such as standing long jumps, jumping onto elevated platforms, and multi-axis jumps.
Guiding Pretraining in Reinforcement Learning with Large Language Models
Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function.
Controllability-Aware Unsupervised Skill Discovery
One of the key capabilities of intelligent agents is the ability to discover useful skills without external supervision.
The Wisdom of Hindsight Makes Language Models Better Instruction Followers
In this paper, we consider an alternative approach: converting feedback to instruction by relabeling the original one and training the model for better alignment in a supervised manner.
Chain of Hindsight Aligns Language Models with Feedback
Applying our method to large language models, we observed that Chain of Hindsight significantly surpasses previous methods in aligning language models with human preferences.
Multi-View Masked World Models for Visual Robotic Manipulation
In this paper, we investigate how to learn good representations with multi-view data and utilize them for visual robotic manipulation.
Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment
Recent progress in scaling up large language models has shown impressive capabilities in performing few-shot learning across a wide range of text-based tasks.
Learning Universal Policies via Text-Guided Video Generation
The proposed policy-as-video formulation can further represent environments with different state and action spaces in a unified space of images, which, for example, enables learning and generalization across a variety of robot manipulation tasks.
Masked Autoencoding for Scalable and Generalizable Decision Making
We are interested in learning scalable agents for reinforcement learning that can learn from large-scale, diverse sequential data similar to current large vision and language models.
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets
Using massive datasets to train large-scale models has emerged as a dominant approach for broad generalization in natural language and vision applications.
VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models
We show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics.
StereoPose: Category-Level 6D Transparent Object Pose Estimation from Stereo Images via Back-View NOCS
To further improve the performance of the stereo framework, StereoPose is equipped with a parallax attention module for stereo feature fusion and an epipolar loss for improving the stereo-view consistency of network predictions.
Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data
In this paper, we address this challenge by presenting Sim2Seg, a re-imagining of RCAN that crosses the visual reality gap for off-road autonomous driving, without using any real-world data.
InstructRL: Simple yet Effective Instruction-Following Agents with Multimodal Transformer
Our \ours method consists of a multimodal transformer that encodes visual observations and language instructions, and a transformer-based policy that predicts actions based on encoded representations.
Dichotomy of Control: Separating What You Can Control from What You Cannot
While return-conditioning is at the heart of popular algorithms such as decision transformer (DT), these methods tend to perform poorly in highly stochastic environments, where an occasional high return can arise from randomness in the environment rather than the actions themselves.
Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models
Large language models (LLM) trained using the next-token-prediction objective, such as GPT3 and PaLM, have revolutionized natural language processing in recent years by showing impressive zero-shot and few-shot capabilities across a wide range of tasks.
Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions
This paper proposes the Virtual MCTS (V-MCTS), a variant of MCTS that spends more search time on harder states and less search time on simpler states adaptively.
CLUTR: Curriculum Learning via Unsupervised Task Representation Learning
Recently, Unsupervised Environment Design (UED) emerged as a new paradigm for zero-shot generalization by simultaneously learning a task distribution and agent policies on the generated tasks.
Skill-Based Reinforcement Learning with Intrinsic Reward Matching
However, often the most concise yet complete description of a task is the reward function itself, and skill learning methods learn an $\textit{intrinsic}$ reward function via the discriminator that corresponds to the skill policy.
Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction
3D bounding boxes are a widespread intermediate representation in many computer vision applications.
Real-World Robot Learning with Masked Visual Pre-training
Finally, we train a 307M parameter vision transformer on a massive collection of 4. 5M images from the Internet and egocentric videos, and demonstrate clearly the benefits of scaling visual pre-training for robot learning.
Temporally Consistent Video Transformer for Long-Term Video Prediction
In this work, we present Temporally Consistent Video Transformer (TECO), a vector-quantized latent dynamics video prediction model that learns compressed representations to efficiently condition on long videos of hundreds of frames during both training and generation.
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks
On a set of 26 benchmark Atari environments, MeanQ outperforms all tested baselines, including the best available baseline, SUNRISE, at 100K interaction steps in 16/26 environments, and by 68% on average.
Multi-Objective Policy Gradients with Topological Constraints
Multi-objective optimization models that encode ordered sequential constraints provide a solution to model various challenging problems including encoding preferences, modeling a curriculum, and enforcing measures of safety.
HARP: Autoregressive Latent Video Prediction with High-Fidelity Image Generator
Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics.
AdaCat: Adaptive Categorical Discretization for Autoregressive Models
Autoregressive generative models can estimate complex continuous data distributions, like trajectory rollouts in an RL environment, image intensities, and audio.
Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision
With continual learning, interventions from the remote pool of humans can also be used to improve the robot fleet control policy over time.
DayDreamer: World Models for Physical Robot Learning
Learning a world model to predict the outcomes of potential actions enables planning in imagination, reducing the amount of trial and error needed in the real environment.
Masked World Models for Visual Control
Yet the current approaches typically train a single model end-to-end for learning both visual representations and dynamics, making it difficult to accurately model the interaction between robots and small objects.
Deep Hierarchical Planning from Pixels
Despite operating in latent space, the decisions are interpretable because the world model can decode goals into images for visualization.
Patch-based Object-centric Transformers for Efficient Video Generation
In this work, we present Patch-based Object-centric Video Transformer (POVT), a novel region-based video generation architecture that leverages object-centric information to efficiently model temporal dynamics in videos.
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning
From these findings, we advocate for evaluating future meta-RL methods on more challenging tasks and including multi-task pretraining with fine-tuning as a simple, yet strong baseline.
Multimodal Masked Autoencoders Learn Transferable Representations
We provide an empirical study of M3AE trained on a large-scale image-text dataset, and find that M3AE is able to learn generalizable representations that transfer well to downstream tasks.
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
Our intuition is that disagreement in learned reward model reflects uncertainty in tailored human feedback and could be useful for exploration.
Chain of Thought Imitation with Procedure Cloning
Imitation learning aims to extract high-performance policies from logged demonstrations of expert behavior.
An Empirical Investigation of Representation Learning for Imitation
We propose a modular framework for constructing representation learning algorithms, then use our framework to evaluate the utility of representation learning for imitation across several environment suites.
Coarse-to-fine Q-attention with Tree Expansion
Coarse-to-fine Q-attention enables sample-efficient robot manipulation by discretizing the translation space in a coarse-to-fine manner, where the resolution gradually increases at each layer in the hierarchy.
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking
To continuously improve the quality of pseudo labels, we iterate the above steps by taking the trained student model as a new teacher and re-label real data using the refined teacher model.
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning
The goal of imitation learning is to mimic expert behavior from demonstrations, without access to an explicit reward signal.
Coarse-to-Fine Q-attention with Learned Path Ranking
We propose Learned Path Ranking (LPR), a method that accepts an end-effector goal pose, and learns to rank a set of goal-reaching paths generated from an array of path generating methods, including: path planning, Bezier curve sampling, and a learned policy.
Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design
We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties with up to 10x more sample efficiency compared to a randomly initialized model.
Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions
We also demonstrate that an effective style reward can be learned from a few seconds of motion capture data gathered from a German Shepherd and leads to energy-efficient locomotion strategies with natural gait transitions.
Reinforcement Learning with Action-Free Pre-Training from Videos
Our framework consists of two phases: we pre-train an action-free latent video prediction model, and then utilize the pre-trained representations for efficiently learning action-conditional world models on unseen environments.
Teachable Reinforcement Learning via Advice Distillation
Training automated agents to complete complex tasks in interactive environments is challenging: reinforcement learning requires careful hand-engineering of reward functions, imitation learning requires specialized infrastructure and access to a human expert, and learning from intermediate forms of supervision (like binary preferences) is time-consuming and extracts little information from each human intervention.
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning
In order to leverage unlabeled samples for reward learning, we infer pseudo-labels of the unlabeled samples based on the confidence of the preference predictor.
It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum Generation
Training such agents efficiently requires automatic generation of a goal curriculum.
Bingham Policy Parameterization for 3D Rotations in Reinforcement Learning
We propose a new policy parameterization for representing 3D rotations during reinforcement learning.
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.
Explaining Reinforcement Learning Policies through Counterfactual Trajectories
In order for humans to confidently decide where to employ RL agents for real-world tasks, a human developer must validate that the agent will perform well at test-time.
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
However, the plans produced naively by LLMs often cannot map precisely to admissible actions.
Target Entropy Annealing for Discrete Soft Actor-Critic
Soft Actor-Critic (SAC) is considered the state-of-the-art algorithm in continuous action space settings.
Zero-Shot Text-Guided Object Generation with Dream Fields
Our method, Dream Fields, can generate the geometry and color of a wide range of objects without 3D supervision.
Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL
Meta-reinforcement learning (meta-RL) has proven to be a successful framework for leveraging experience from prior tasks to rapidly learn new related tasks, however, current meta-RL approaches struggle to learn in sparse reward environments.
Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning
Maximum Entropy Reinforcement Learning (MaxEnt RL) algorithms such as Soft Q-Learning (SQL) and Soft Actor-Critic trade off reward and policy entropy, which has the potential to improve training stability and robustness.
B-Pref: Benchmarking Preference-Based Reinforcement Learning
However, it is difficult to quantify the progress in preference-based RL due to the lack of a commonly adopted benchmark.
Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning
We show that a single generalist policy can perform in-hand manipulation of over 100 geometrically-diverse real-world objects and generalize to new objects with unseen shape or size.
Mastering Atari Games with Limited Data
Recently, there has been significant progress in sample efficient image-based RL algorithms; however, consistent human-level performance on the Atari game benchmark remains an elusive goal.
Ranked #1 on
Atari Games 100k
on Atari 100k
Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates
Under the belief that $\beta$ is closely related to the (state dependent) model uncertainty, Entropy Regularized Q-Learning (EQL) further introduces a principled scheduling of $\beta$ by maintaining a collection of the model parameters that characterizes model uncertainty.
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.
Towards More Generalizable One-shot Visual Imitation Learning
We then study the multi-task setting, where multi-task training is followed by (i) one-shot imitation on variations within the training tasks, (ii) one-shot imitation on new tasks, and (iii) fine-tuning on new tasks.
Pretraining for Language Conditioned Imitation with Transformers
We study reinforcement learning (RL) agents which can utilize language inputs.
Semi-supervised Offline Reinforcement Learning with Pre-trained Decision Transformers
In this paper, we investigate how we can leverage large reward-free (i. e. task-agnostic) offline datasets of prior interactions to pre-train agents that can then be fine-tuned using a small reward-annotated dataset.
Autoregressive Latent Video Prediction with High-Fidelity Image Generator
Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics.
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation
We are interested in training general-purpose reinforcement learning agents that can solve a wide variety of goals.
Improving Long-Horizon Imitation Through Language Prediction
Complex, long-horizon planning and its combinatorial nature pose steep challenges for learning-based agents.
APS: Active Pretraining with Successor Features
We introduce a new unsupervised pretraining objective for reinforcement learning.
Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback
A promising approach to solving challenging long-horizon tasks has been to extract behavior priors (skills) by fitting generative models to large offline datasets of demonstrations.
Playful Interactions for Representation Learning
In this work, we propose to use playful interactions in a self-supervised manner to learn visual representations for downstream tasks.
Hierarchical Few-Shot Imitation with Skill Transition Models
To this end, we present Few-shot Imitation with Skill Transition Models (FIST), an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks given a few downstream demonstrations.
The MineRL BASALT Competition on Learning from Human Feedback
Rather than training AI systems using a predefined reward function or using a labeled dataset with a predefined set of categories, we instead train the AI system using a learning signal derived from some form of human feedback, which can evolve over time as the understanding of the task changes, or as the capabilities of the AI system improve.
Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble
Recent advance in deep offline reinforcement learning (RL) has made it possible to train strong robotic agents from offline datasets.
Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments
Furthermore, in complex domains such as soccer, the space of possible scenarios is infinite, which makes it impossible for one research group to provide a comprehensive set of scenarios to train, test, and benchmark RL algorithms.
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL
When combined together, they substantially improve the performance and generalization of offline RL policies.
Unsupervised Learning of Visual 3D Keypoints for Control
Prior works show that structured latent space such as visual keypoints often outperforms unstructured representations for robotic control.
Data-Efficient Exploration with Self Play for Atari
To alleviate the reliance on reward engineering it is important to develop RL algorithms capable of efficiently acquiring skills with no rewards extrinsic to the agent.
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
We also show that our method is able to utilize real-time human feedback to effectively prevent reward exploitation and learn new behaviors that are difficult to specify with standard reward functions.
JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data
Such a decomposition can dynamically control the reliability of information derived from the online and offline data and the use of pretrained neural networks permits scalability to large offline datasets.
Decision Transformer: Reinforcement Learning via Sequence Modeling
In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling.
Ranked #42 on
Atari Games
on Atari 2600 Pong
(using extra training data)
VideoGPT: Video Generation using VQ-VAE and Transformers
We present VideoGPT: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos.
Auto-Tuned Sim-to-Real Transfer
Policies trained in simulation often fail when transferred to the real world due to the `reality gap' where the simulator is unable to accurately capture the dynamics and visual properties of the real world.
Learning What To Do by Simulating the Past
Since reward functions are hard to specify, recent work has focused on learning policies from human feedback.
GEM: Group Enhanced Model for Learning Dynamical Control Systems
In this work, we take advantage of these structures to build effective dynamical models that are amenable to sample-based learning.
AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control
Our system produces high-quality motions that are comparable to those achieved by state-of-the-art tracking-based techniques, while also being able to easily accommodate large datasets of unstructured motion clips.
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis
We present DietNeRF, a 3D neural scene representation estimated from a few images.
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots
Developing robust walking controllers for bipedal robots is a challenging endeavor.
Mutual Information State Intrinsic Control
Reinforcement learning has been shown to be highly successful at many challenging tasks.
Pretrained Transformers as Universal Computation Engines
We investigate the capability of a transformer pretrained on natural language to generalize to other modalities with minimal finetuning -- in particular, without finetuning of the self-attention and feedforward layers of the residual blocks.
Behavior From the Void: Unsupervised Active Pre-Training
We introduce a new unsupervised pre-training method for reinforcement learning called APT, which stands for Active Pre-Training.
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
Recent advances in off-policy deep reinforcement learning (RL) have led to impressive success in complex tasks from visual observations.
Ranked #32 on
Atari Games
on Atari 2600 Amidar
Task-Agnostic Morphology Evolution
Deep reinforcement learning primarily focuses on learning behavior, usually overlooking the fact that an agent's function is largely determined by form.
State Entropy Maximization with Random Encoders for Efficient Exploration
Recent exploration methods have proven to be a recipe for improving sample-efficiency in deep reinforcement learning (RL).
MSA Transformer
Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins.
Bottleneck Transformers for Visual Recognition
Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84. 7% top-1 accuracy on the ImageNet benchmark while being up to 1. 64x faster in compute time than the popular EfficientNet models on TPU-v3 hardware.
Ranked #37 on
Instance Segmentation
on COCO minival
Reinforcement Learning with Latent Flow
Temporal information is essential to learning effective policies with Reinforcement Learning (RL).
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.
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.
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.
Robust Imitation via Decision-Time Planning
The goal of imitation learning is to mimic expert behavior from demonstrations, without access to an explicit reward signal.
R-LAtte: Attention Module for Visual Control via Reinforcement Learning
Attention mechanisms are generic inductive biases that have played a critical role in improving the state-of-the-art in supervised learning, unsupervised pre-training and generative modeling for multiple domains including vision, language and speech.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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
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.
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.
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).
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
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
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.
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.
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.
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.
Plan2Vec: Unsupervised Representation Learning by Latent Plans
In this paper we introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning.
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.
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)
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.
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.
Hierarchically Decoupled Imitation for Morphological Transfer
Learning long-range behaviors on complex high-dimensional agents is a fundamental problem in robot learning.
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.
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.
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.
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.
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.
Preventing Imitation Learning with Adversarial Policy Ensembles
To our knowledge, this is the first work regarding the protection of policies in Reinforcement Learning.
Hierarchical Variational Imitation Learning of Control Programs
Autonomous agents can learn by imitating teacher demonstrations of the intended behavior.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.