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Found 15 papers, 8 papers with code
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training.
Modulated Policy Hierarchies
To achieve this, we study different modulation signals and exploration for hierarchical controllers.
Learning Latent Dynamics for Planning from Pixels
Planning has been very successful for control tasks with known environment dynamics.
Ranked #2 on
Continuous Control
on DeepMind Walker Walk (Images)
Interpretable Intuitive Physics Model
In order to demonstrate that our system models these underlying physical properties, we train our model on collisions of different shapes (cube, cone, cylinder, spheres etc.)
Noise Contrastive Priors for Functional Uncertainty
NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training.
Visual Representations for Semantic Target Driven Navigation
We propose to using high level semantic and contextual features including segmentation and detection masks obtained by off-the-shelf state-of-the-art vision as observations and use deep network to learn the navigation policy.
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning
The RL agents learn short-range, point-to-point navigation policies that capture robot dynamics and task constraints without knowledge of the large-scale topology.
TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow
We introduce TensorFlow Agents, an efficient infrastructure paradigm for building parallel reinforcement learning algorithms in TensorFlow.
Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations
Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations.
A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors
Domain knowledge can often be encoded in the structure of a network, such as convolutional layers for vision, which has been shown to increase generalization and decrease sample complexity, or the number of samples required for successful learning.
Learning Hierarchical Information Flow with Recurrent Neural Modules
We propose ThalNet, a deep learning model inspired by neocortical communication via the thalamus.
Discrete Sequential Prediction of Continuous Actions for Deep RL
Specifically, we show how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions.
Robust Adversarial Reinforcement Learning
Deep neural networks coupled with fast simulation and improved computation have led to recent successes in the field of reinforcement learning (RL).
Cognitive Mapping and Planning for Visual Navigation
The accumulated belief of the world enables the agent to track visited regions of the environment.
Supervision via Competition: Robot Adversaries for Learning Tasks
Due to large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success/failure.
