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Found 34 papers, 19 papers with code
IL-flOw: Imitation Learning from Observation using Normalizing Flows
We present an algorithm for Inverse Reinforcement Learning (IRL) from expert state observations only.
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
In this work, we study the use of the Bellman equation as a surrogate objective for value prediction accuracy.
Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain
We present a reward-predictive, model-based deep learning method featuring trajectory-constrained visual attention for use in mapless, local visual navigation tasks.
Multi-batch Reinforcement Learning via Sample Transfer and Imitation Learning
Unfortunately, most online reinforcement learning algorithms require a large number of interactions with the environment to learn a reliable control policy.
Generalizing Successor Features to continuous domains for Multi-task Learning
In this paper, we consider the problem of continuous control for various robot manipulation tasks with an explicit representation that promotes skill reuse while learning multiple tasks, related through the reward function.
Why Should I Trust You, Bellman? Evaluating the Bellman Objective with Off-Policy Data
In this work, we analyze the effectiveness of the Bellman equation as a proxy objective for value prediction accuracy in off-policy evaluation.
Active 3D Shape Reconstruction from Vision and Touch
In this paper, we focus on this problem and introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2)a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile signals; and 3) a set of data-driven solutions with either tactile or visuotactile priors to guide the shape exploration.
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation
We bridge the gap between MIS and deep reinforcement learning by observing that the density ratio can be computed from the successor representation of the target policy.
Learning Intuitive Physics with Multimodal Generative Models
Predicting the future interaction of objects when they come into contact with their environment is key for autonomous agents to take intelligent and anticipatory actions.
Practical Marginalized Importance Sampling with the Successor Representation
We bridge the gap between MIS and deep reinforcement learning by observing that the density ratio can be computed from the successor representation of the target policy.
Intervention Design for Effective Sim2Real Transfer
The goal of this work is to address the recent success of domain randomization and data augmentation for the sim2real setting.
Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference
Utilization of latent space to capture a lower-dimensional representation of a complex dynamics model is explored in this work.
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
Prioritized Experience Replay (PER) is a deep reinforcement learning technique in which agents learn from transitions sampled with non-uniform probability proportionate to their temporal-difference error.
3D Shape Reconstruction from Vision and Touch
When a toddler is presented a new toy, their instinctual behaviour is to pick it upand inspect it with their hand and eyes in tandem, clearly searching over its surface to properly understand what they are playing with.
Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images
We present a method for learning to drive on smooth terrain while simultaneously avoiding collisions in challenging off-road and unstructured outdoor environments using only visual inputs.
Detecting GAN generated errors
This is because the task of evaluating the quality of a generated image differs from deciding if an image is real or fake.
Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales
PAC Bayes is a generalized framework which is more resistant to overfitting and that yields performance bounds that hold with arbitrarily high probability even on the unjustified extrapolations.
Deep learning for Aerosol Forecasting
Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against ground truth.
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning
Motivated by the recursive Newton-Euler formulation, we propose a novel cascaded Gaussian process learning framework for the inverse dynamics of robot manipulators.
Learning Domain Randomization Distributions for Training Robust Locomotion Policies
We explore the use of gradient-based search methods to learn a domain randomization with the following properties: 1) The trained policy should be successful in environments sampled from the domain randomization distribution 2) The domain randomization distribution should be wide enough so that the experience similar to the target robot system is observed during training, while addressing the practicality of training finite capacity models.
Human Motion Prediction via Pattern Completion in Latent Representation Space
Inspired by ideas in cognitive science, we propose a novel and general approach to solve human motion understanding via pattern completion on a learned latent representation space.
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
Learned controllers such as neural networks typically do not have a notion of uncertainty that allows to diagnose an offset between training and testing conditions, and potentially intervene.
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
Mesh models are a promising approach for encoding the structure of 3D objects.
Ranked #1 on
3D Object Reconstruction
on Data3D−R2N2
(Avg F1 metric)
Off-Policy Deep Reinforcement Learning without Exploration
Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, without offering further possibility for data collection.
Where Off-Policy Deep Reinforcement Learning Fails
This work examines batch reinforcement learning--the task of maximally exploiting a given batch of off-policy data, without further data collection.
Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning
Finally, we assess the performance of the algorithm for learning motor controllers for a six legged autonomous underwater vehicle.
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
We consider the problem of scaling deep generative shape models to high-resolution.
Ranked #2 on
3D Object Reconstruction
on Data3D−R2N2
(Avg F1 metric)
Addressing Function Approximation Error in Actor-Critic Methods
In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies.
Bayesian Policy Gradients via Alpha Divergence Dropout Inference
Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult.
Cost Adaptation for Robust Decentralized Swarm Behaviour
To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic.
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning
Inverse reinforcement learning offers a useful paradigm to learn the underlying reward function directly from expert demonstrations.
Deep Reinforcement Learning that Matters
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL).
Benchmark Environments for Multitask Learning in Continuous Domains
As demand drives systems to generalize to various domains and problems, the study of multitask, transfer and lifelong learning has become an increasingly important pursuit.
Improved Adversarial Systems for 3D Object Generation and Reconstruction
This paper describes a new approach for training generative adversarial networks (GAN) to understand the detailed 3D shape of objects.
