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Found 8 papers, 3 papers with code
Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots
An oft-ignored challenge of real-world reinforcement learning is that the real world does not pause when agents make learning updates.
Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination
We study the problem of training a Reinforcement Learning (RL) agent that is collaborative with humans without using any human data.
Receptive Multi-granularity Representation for Person Re-Identification
Current stripe-based feature learning approaches have delivered impressive accuracy, but do not make a proper trade-off between diversity, locality, and robustness, which easily suffers from part semantic inconsistency for the conflict between rigid partition and misalignment.
Accurate Temporal Action Proposal Generation with Relation-Aware Pyramid Network
Accurate temporal action proposals play an important role in detecting actions from untrimmed videos.
Relation-Aware Pyramid Network (RapNet) for temporal action proposal
In this technical report, we describe our solution to temporal action proposal (task 1) in ActivityNet Challenge 2019.
Meta Reinforcement Learning with Distribution of Exploration Parameters Learned by Evolution Strategies
In this paper, we propose a novel meta-learning method in a reinforcement learning setting, based on evolution strategies (ES), exploration in parameter space and deterministic policy gradients.
Continual Match Based Training in Pommerman: Technical Report
We show in the experiments that Pommerman is a perfect environment for studying continual learning, and the agent can improve its performance by continually learning new skills without forgetting the old ones.
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities.
Ranked #3 on
Person Re-Identification
on SYSU-30k
(using extra training data)
