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Found 21 papers, 9 papers with code
Deep Learning for Day Forecasts from Sparse Observations
The ability of neural models to make a prediction in less than a second once the data is available and to do so with very high temporal and spatial resolution, and the ability to learn directly from atmospheric observations, are just some of these models' unique advantages.
Continuous Control With Ensemble Deep Deterministic Policy Gradients
The growth of deep reinforcement learning (RL) has brought multiple exciting tools and methods to the field.
What Matters for Adversarial Imitation Learning?
To tackle this issue, we implement more than 50 of these choices in a generic adversarial imitation learning framework and investigate their impacts in a large-scale study (>500k trained agents) with both synthetic and human-generated demonstrations.
Hyperparameter Selection for Imitation Learning
The vast literature in imitation learning mostly considers this reward function to be available for HP selection, but this is not a realistic setting.
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
Solving Rubik's Cube with a Robot Hand
We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot.
Learning Dexterous In-Hand Manipulation
We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand.
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
The purpose of this technical report is two-fold.
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.
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.
Overcoming Exploration in Reinforcement Learning with Demonstrations
Exploration in environments with sparse rewards has been a persistent problem in reinforcement learning (RL).
Hindsight Experience Replay
Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL).
Parameter Space Noise for Exploration
Combining parameter noise with traditional RL methods allows to combine the best of both worlds.
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 to learn by gradient descent by gradient descent
The move from hand-designed features to learned features in machine learning has been wildly successful.
Learning Efficient Algorithms with Hierarchical Attentive Memory
In this paper, we propose and investigate a novel memory architecture for neural networks called Hierarchical Attentive Memory (HAM).
Neural Random-Access Machines
In this paper, we propose and investigate a new neural network architecture called Neural Random Access Machine.
Modeling Bitcoin Contracts by Timed Automata
We hope that our work can draw the attention of the researchers working on formal modeling to the problem of the Bitcoin contract verification, and spark off more research on this topic.
Cryptography and Security
