Search Results for author:
Found 26 papers, 4 papers with code
Cross-domain Random Pre-training with Prototypes for Reinforcement Learning
Moreover, prototypical representation learning with a novel intrinsic loss is proposed to pre-train an effective and generic encoder across different domains.
STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation
By fitting a bridge-shaped curve to the illumination map distribution, both regions are suppressed and two tasks are bridged naturally.
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat
Then the inner loop tracks the macro behavior with a flight controller by calculating the actual input signals for the aircraft.
Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning
The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse environments with unseen dynamics remains challenging.
Model-based Reinforcement Learning
reinforcement-learning
+1
Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation
Our CGTP framework is an end to end and interpretable model, including three main stages: context encoding, goal interactive prediction and trajectory interactive prediction.
TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving
In particular, TrajGen consists of the multi-modal trajectory prediction stage and the reinforcement learning based trajectory modification stage.
Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic
In this paper, we formulate a multi-task safe reinforcement learning with social attention to improve the safety and efficiency when interacting with other traffic participants.
BViT: Broad Attention based Vision Transformer
In this paper, we propose the broad attention to improve the performance by incorporating the attention relationship of different layers for vision transformer, which is called BViT.
Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search
Moreover, multi-scale feature fusion and knowledge embedding are proposed to improve the performance of BCNN with shallow topology.
Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning
We train several state-of-the-art deep reinforcement learning methods in the designed training scenarios and provide the benchmark metrics evaluation results of the trained models in the test scenarios.
A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios
The test benchmark and baselines are to provide a fair and comprehensive training and testing platform for the study of RL for autonomous driving in the intersection scenario, advancing the progress of RL-based methods for intersection autonomous driving control.
Event-Triggered Multi-agent Reinforcement Learning with Communication under Limited-bandwidth Constraint
Then the design of the event-triggered strategy is formulated as a constrained Markov decision problem, and reinforcement learning finds the best communication protocol that satisfies the limited bandwidth constraint.
Multiagent Systems
Heuristic Rank Selection with Progressively Searching Tensor Ring Network
Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region.
Dynamic Horizon Value Estimation for Model-based Reinforcement Learning
Existing model-based value expansion methods typically leverage a world model for value estimation with a fixed rollout horizon to assist policy learning.
Model-based Reinforcement Learning
reinforcement-learning
+1
BNAS-v2: Memory-efficient and Performance-collapse-prevented Broad Neural Architecture Search
For this consequent issue, two solutions are given: 1) we propose Confident Learning Rate (CLR) that considers the confidence of gradient for architecture weights update, increasing with the training time of over-parameterized BCNN; 2) we introduce the combination of partial channel connections and edge normalization that also can improve the memory efficiency further.
ContourRend: A Segmentation Method for Improving Contours by Rendering
A good object segmentation should contain clear contours and complete regions.
BiFNet: Bidirectional Fusion Network for Road Segmentation
Considering the bird's eye views(BEV) of the LiDAR remains the space structure in horizontal plane, this paper proposes a bidirectional fusion network(BiFNet) to fuse the image and BEV of the point cloud.
ModuleNet: Knowledge-inherited Neural Architecture Search
Unlike previous search algorithms, and benefiting from inherited knowledge, our method is able to directly search for architectures in the macro space by NSGA-II algorithm without tuning parameters in these \textit{module}s. Experiments show that our strategy can efficiently evaluate the performance of new architecture even without tuning weights in convolutional layers.
Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning: Results for the Fighting Game AI Competition
In contrast to conventional RHEA, an opponent model is proposed and is optimized by supervised learning with cross-entropy and reinforcement learning with policy gradient and Q-learning respectively, based on history observations from opponent.
BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture
In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve the above issue.
Graph-FCN for image semantic segmentation
Then we apply graph convolutional network to solve this graph node classification problem.
A Survey of Deep Reinforcement Learning in Video Games
In this paper, we survey the progress of DRL methods, including value-based, policy gradient, and model-based algorithms, and compare their main techniques and properties.
Graph Attention Memory for Visual Navigation
To address the long-term memory issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module and control module.
Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints
Autonomous driving decision-making is a great challenge due to the complexity and uncertainty of the traffic environment.
Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving
The control module which is based on reinforcement learning then makes a control decision based on these features.
StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning
With reinforcement learning and curriculum transfer learning, our units are able to learn appropriate strategies in StarCraft micromanagement scenarios.
