Search Results for author:
Found 48 papers, 17 papers with code
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.
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
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
OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control
In this work, we introduce OmniDrones, an efficient and flexible platform tailored for reinforcement learning in drone control, built on Nvidia's Omniverse Isaac Sim.
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.
Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased
A crucial limitation of this framework is that every policy in the pool is optimized w. r. t.
CASTER: A Computer-Vision-Assisted Wireless Channel Simulator for Gesture Recognition
In the proposed CASTER simulator, however, the training dataset can be simulated via existing videos.
LLM-Powered Hierarchical Language Agent for Real-time Human-AI Coordination
AI agents powered by Large Language Models (LLMs) have made significant advances, enabling them to assist humans in diverse complex tasks and leading to a revolution in human-AI coordination.
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.
Safe Offline Reinforcement Learning with Real-Time Budget Constraints
Aiming at promoting the safe real-world deployment of Reinforcement Learning (RL), research on safe RL has made significant progress in recent years.
Automatic Truss Design with Reinforcement Learning
Directly applying end-to-end reinforcement learning (RL) methods to truss layout design is infeasible either, since only a tiny portion of the entire layout space is valid under the physical constraints, leading to particularly sparse rewards for RL training.
Plan To Predict: Learning an Uncertainty-Foreseeing Model for Model-Based Reinforcement Learning
In Model-based Reinforcement Learning (MBRL), model learning is critical since an inaccurate model can bias policy learning via generating misleading samples.
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration
Simply waiting for every robot being ready for the next action can be particularly time-inefficient.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Off-Policy Primal-Dual Safe Reinforcement Learning
Results on benchmark tasks show that our method not only achieves an asymptotic performance comparable to state-of-the-art on-policy methods while using much fewer samples, but also significantly reduces constraint violation during training.
Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation
Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge by introducing a hierarchical structure to decompose the search space, where the low-level policy predicts primitive actions in the guidance of the goals derived from the high-level policy.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
+2
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement Learning
Recently, Multi-Agent Reinforcement Learning (MARL) has been applied to a large number of scenarios and has shown promising performance.
Policy-regularized Offline Multi-objective Reinforcement Learning
In this paper, we aim to utilize only offline trajectory data to train a policy for multi-objective RL.
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.
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.
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.
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.
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.
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.
Aspect-Based Sentiment Analysis
Aspect-oriented Opinion Extraction
+6
Single-photon imaging over 200 km
Long-range active imaging has widespread applications in remote sensing and target recognition.
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.
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.
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.
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.
Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning
Despite of achieving great success in real-world applications, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, i. e., data efficiency, lack of the interpretability and transferability.
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.
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.
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
+2
Causal Deep Reinforcement Learning Using Observational Data
Offline reinforcement learning promises to alleviate this issue by exploiting the vast amount of observational data available in the real world.
mmAlert: mmWave Link Blockage Prediction via Passive Sensing
It is demonstrated via experiments that the mmAlert system can always detect the motions of the walking person close to the LoS path, and predict 90\% of the LoS blockage with sensing time of 1. 4 seconds.
Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey
Reinforcement Learning(RL) has achieved tremendous development in recent years, but still faces significant obstacles in addressing complex real-life problems due to the issues of poor system generalization, low sample efficiency as well as safety and interpretability concerns.
Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learning
Exploring sparse reward multi-agent reinforcement learning (MARL) environments with traps in a collaborative manner is a complex task.
SIFTER: A Task-specific Alignment Strategy for Enhancing Sentence Embeddings
This indicates that different downstream tasks have different levels of sensitivity to sentence components.
AlphaZero Gomoku
In the past few years, AlphaZero's exceptional capability in mastering intricate board games has garnered considerable interest.
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games
Alternatively, Policy-Space Response Oracles (PSRO) is an iterative framework for learning NE, where the best responses w. r. t.
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning
In this work, we present a novel subgame curriculum learning framework for zero-sum games.
Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game
To mitigate the intrinsic bias in language actions, our agents use an LLM to perform deductive reasoning and generate a diverse set of action candidates.
Active Neural Topological Mapping for Multi-Agent Exploration
In this paper, we propose Multi-Agent Neural Topological Mapping (MANTM) to improve exploration efficiency and generalization for multi-agent exploration tasks.
Passive Handwriting Tracking via Weak mmWave Communication Signals
In this letter, a cooperative sensing framework based on millimeter wave (mmWave) communication systems is proposed to detect tiny motions with a millimeter-level resolution.
MASP: Scalable GNN-based Planning for Multi-Agent Navigation
Besides, to enhance generalization capabilities in scenarios with unseen team sizes, we divide agents into multiple groups, each with a previously trained number of agents.
TaskFlex Solver for Multi-Agent Pursuit via Automatic Curriculum Learning
In this work, we combine RL and curriculum learning to introduce a flexible solver for multiagent pursuit problems, named TaskFlex Solver (TFS), which is capable of solving multi-agent pursuit problems with diverse and dynamically changing task conditions in both 2-dimensional and 3-dimensional scenarios.
Multi-Agent Reinforcement Learning with a Hierarchy of Reward Machines
In this paper, we study the cooperative Multi-Agent Reinforcement Learning (MARL) problems using Reward Machines (RMs) to specify the reward functions such that the prior knowledge of high-level events in a task can be leveraged to facilitate the learning efficiency.
Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study
However, in academic benchmarks, state-of-the-art results are often achieved via reward-free methods, such as Direct Preference Optimization (DPO).
