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Found 23 papers, 9 papers with code
Reachability Constrained Reinforcement Learning
We characterize the feasible set by the established self-consistency condition, then a safety value function can be learned and used as constraints in CRL.
Scale-Equivalent Distillation for Semi-Supervised Object Detection
Further, we overcome these challenges by introducing a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance.
Zeroth-Order Actor-Critic
Zeroth-order optimization methods and policy gradient based first-order methods are two promising alternatives to solve reinforcement learning (RL) problems with complementary advantages.
Model-Based Reinforcement Learning via Imagination with Derived Memory
Model-based reinforcement learning aims to improve the sample efficiency of policy learning by modeling the dynamics of the environment.
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning
Existing methods mostly use the posterior penalty for dangerous actions, which means that the agent is not penalized until experiencing danger.
Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning
This paper proposes a novel approach that simultaneously synthesizes the energy-function-based safety certificate and learns the safe control policy with CRL.
Flow-based Recurrent Belief State Learning for POMDPs
Partially Observable Markov Decision Process (POMDP) provides a principled and generic framework to model real world sequential decision making processes but yet remains unsolved, especially for high dimensional continuous space and unknown models.
Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian
Based on this, the penalty method is formulated as a proportional controller, and the Lagrangian method is formulated as an integral controller.
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety
The safety constraints commonly used by existing safe reinforcement learning (RL) methods are defined only on expectation of initial states, but allow each certain state to be unsafe, which is unsatisfying for real-world safety-critical tasks.
BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings.
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Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function
Model information can be used to predict future trajectories, so it has huge potential to avoid dangerous region when implementing reinforcement learning (RL) on real-world tasks, like autonomous driving.
Mixed Policy Gradient
MPG contains two types of PG: 1) data-driven PG, which is obtained by directly calculating the derivative of the learned Q-value function with respect to actions, and 2) model-driven PG, which is calculated using BPTT based on the model-predictive return.
Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning
Taking a control perspective, we first interpret the penalty method and the Lagrangian method as proportional feedback and integral feedback control, respectively.
Steadily Learn to Drive with Virtual Memory
Reinforcement learning has shown great potential in developing high-level autonomous driving.
A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
To address this challenge, we propose a hierarchical behavior planning framework with a set of low-level safe controllers and a high-level reinforcement learning algorithm (H-CtRL) as a coordinator for the low-level controllers.
Model-Based Actor-Critic with Chance Constraint for Stochastic System
Safety is essential for reinforcement learning (RL) applied in real-world situations.
Efficient Deep Learning of Non-local Features for Hyperspectral Image Classification
Therefore, to incorporate the long-range contextual information, a deep fully convolutional network (FCN) with an efficient non-local module, named ENL-FCN, is proposed for HSI classification.
End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning
Current autonomous driving systems are composed of a perception system and a decision system.
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
A sequential latent environment model is introduced and learned jointly with the reinforcement learning process.
Adaptive Probabilistic Vehicle Trajectory Prediction Through Physically Feasible Bayesian Recurrent Neural Network
Current methods for long-term trajectory prediction cannot guarantee the physical feasibility of predicted distribution.
The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform
We present the design and implementation of a visual search system for real time image retrieval on JD. com, the world's third largest and China's largest e-commerce site.
Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks
As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles.
Model-free Deep Reinforcement Learning for Urban Autonomous Driving
Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions.
