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Found 15 papers, 5 papers with code
Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution
Recent reinforcement learning approaches have shown surprisingly strong capabilities of bang-bang policies for solving continuous control benchmarks.
OptFlow: Fast Optimization-based Scene Flow Estimation without Supervision
Without relying on learning or any labeled datasets, OptFlow achieves state-of-the-art performance for scene flow estimation on popular autonomous driving benchmarks.
Solving Continuous Control via Q-learning
While there has been substantial success for solving continuous control with actor-critic methods, simpler critic-only methods such as Q-learning find limited application in the associated high-dimensional action spaces.
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks
Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents.
Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models
Fundamental to the success of our method is the design of a novel multi-agent policy network that can steer a vehicle given the state of the surrounding agents and the map information.
VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles
Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios.
Learning Interactive Driving Policies via Data-driven Simulation
Data-driven simulators promise high data-efficiency for driving policy learning.
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space.
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
We demonstrate the effectiveness of our algorithm in learning competitive behaviors on a novel multi-agent racing benchmark that requires planning from image observations.
Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles
Learning complex robot behaviors through interaction requires structured exploration.
Learning to Plan via Deep Optimistic Value Exploration
Deep exploration requires coordinated long-term planning.
Deep Orientation Uncertainty Learning based on a Bingham Loss
Reasoning about uncertain orientations is one of the core problems in many perception tasks such as object pose estimation or motion estimation.
Deep Evidential Regression
We demonstrate learning well-calibrated measures of uncertainty on various benchmarks, scaling to complex computer vision tasks, as well as robustness to adversarial and OOD test samples.
Deep Evidential Uncertainty
In this paper, we propose a novel method for training deterministic NNs to not only estimate the desired target but also the associated evidence in support of that target.
Training Support Vector Machines using Coresets
We present a novel coreset construction algorithm for solving classification tasks using Support Vector Machines (SVMs) in a computationally efficient manner.
