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Found 21 papers, 5 papers with code
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning
Despite all the advantages, we revisit these two principles and show that in certain scenarios, e. g., environments with a highly multi-modal reward landscape, value decomposition, and parameter sharing can be problematic and lead to undesired outcomes.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
ESCM$^2$: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation
In this paper, we theoretically demonstrate that ESMM suffers from the following two problems: (1) Inherent Estimation Bias (IEB), where the estimated CVR of ESMM is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where there is a risk that the ESMM overlooks the causality from click to conversion.
Constrained Sequence-to-Tree Generation for Hierarchical Text Classification
Hierarchical Text Classification (HTC) is a challenging task where a document can be assigned to multiple hierarchically structured categories within a taxonomy.
Passive Motion Detection via mmWave Communication System
Relying on the passive sensing system, a dataset of received signals, where three types of hand gestures are sensed, is collected by using Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) paths as the reference channel respectively.
Creativity of AI: Automatic Symbolic Option Discovery for Facilitating Deep Reinforcement Learning
Despite of achieving great success in real life, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, which are data efficiency, lack of the interpretability and transferability.
Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward
These scenarios indeed correspond to the vulnerabilities of the under-test driving policies, thus are meaningful for their further improvements.
Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines
In this paper, we propose Lifelong reinforcement learning with Sequential linear temporal logic formulas and Reward Machines (LSRM), which enables an agent to leverage previously learned knowledge to fasten learning of logically specified tasks.
Coordinated Proximal Policy Optimization
We present Coordinated Proximal Policy Optimization (CoPPO), an algorithm that extends the original Proximal Policy Optimization (PPO) to the multi-agent setting.
Learning Efficient Multi-Agent Cooperative Visual Exploration
In this paper, we extend the state-of-the-art single-agent visual navigation method, Active Neural SLAM (ANS), to the multi-agent setting by introducing a novel RL-based planning module, Multi-agent Spatial Planner (MSP). MSP leverages a transformer-based architecture, Spatial-TeamFormer, which effectively captures spatial relations and intra-agent interactions via hierarchical spatial self-attentions.
Reinforcement Learning with Expert Trajectory For Quantitative Trading
In recent years, quantitative investment methods combined with artificial intelligence have attracted more and more attention from investors and researchers.
Single-photon imaging over 200 km
Long-range active imaging has widespread applications in remote sensing and target recognition.
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization
We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games.
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
Proximal Policy Optimization (PPO) is a popular on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings.
A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis
Some recent work focused on solving a combination of two subtasks, e. g., extracting aspect terms along with sentiment polarities or extracting the aspect and opinion terms pair-wisely.
Ranked #2 on
Aspect Sentiment Triplet Extraction
on SemEval
Aspect-oriented Opinion Extraction
Aspect Sentiment Triplet Extraction
+3
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms
We benchmark commonly used multi-agent deep reinforcement learning (MARL) algorithms on a variety of cooperative multi-agent games.
Deep Learning-based Modulation Detection for NOMA Systems
Since the signal with strong power should be demodulated first for successive interference cancellation (SIC) demodulation in non-orthogonal multiple access (NOMA) systems, the base station (BS) should inform the near user terminal (UT), which has allocated higher power, of modulation mode of the far user terminal.
Symmetrical Gaussian Error Linear Units (SGELUs)
In this paper, a novel neural network activation function, called Symmetrical Gaussian Error Linear Unit (SGELU), is proposed to obtain high performance.
Reinforcement Learning in Healthcare: A Survey
As a subfield of machine learning, reinforcement learning (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback.
Learning Shaping Strategies in Human-in-the-loop Interactive Reinforcement Learning
Providing reinforcement learning agents with informationally rich human knowledge can dramatically improve various aspects of learning.
The Price of Governance: A Middle Ground Solution to Coordination in Organizational Control
We then propose a hierarchical supervision framework to explicitly model the PoG, and define step by step how to realize the core principle of the framework and compute the optimal PoG for a control problem.
DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments
It is one of the state-of-the-art SLAM systems in high-dynamic environments.
Robotics
