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Found 26 papers, 3 papers with code
Improving Efficiency of DNN-based Relocalization Module for Autonomous Driving with Server-side Computing
Our findings highlight the vital role of server-side offloading in DNN-based camera relocation for autonomous vehicles, and we also discuss the results of data fusion.
Let Models Speak Ciphers: Multiagent Debate through Embeddings
Although natural language is an obvious choice for communication due to LLM's language understanding capability, the token sampling step needed when generating natural language poses a potential risk of information loss, as it uses only one token to represent the model's belief across the entire vocabulary.
Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency
Specifically, we design a prompt template for reasoning that learns from the memory buffer and plans a future trajectory over a long horizon ("reason for future").
Differentiable Arbitrating in Zero-sum Markov Games
We initiate the study of how to perturb the reward in a zero-sum Markov game with two players to induce a desirable Nash equilibrium, namely arbitrating.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
An Efficient Approach to the Online Multi-Agent Path Finding Problem by Using Sustainable Information
The low-level solver, the Sustainable Reverse Safe Interval Path Planning algorithm (SRSIPP), is an efficient single-agent solver that uses previous planning context to reduce duplicate calculations.
An Analysis of Attention via the Lens of Exchangeability and Latent Variable Models
In particular, such a representation instantiates the posterior distribution of the latent variable given input tokens, which plays a central role in predicting output labels and solving downstream tasks.
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL
The cooperative Multi-A gent R einforcement Learning (MARL) with permutation invariant agents framework has achieved tremendous empirical successes in real-world applications.
Differentiable Bilevel Programming for Stackelberg Congestion Games
A Stackelberg congestion game (SCG) is a bilevel program in which a leader aims to maximize their own gain by anticipating and manipulating the equilibrium state at which followers settle by playing a congestion game.
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction
Last, an objective function is designed to derive the future OD demands according to the most recent node representations, and also to tackle the data sparsity problem in OD prediction.
BooVI: Provably Efficient Bootstrapped Value Iteration
Despite the tremendous success of reinforcement learning (RL) with function approximation, efficient exploration remains a significant challenge, both practically and theoretically.
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence
To regulate a social system comprised of self-interested agents, economic incentives are often required to induce a desirable outcome.
Foreground Object Structure Transfer for Unsupervised Domain Adaptation
In practice, since the label of the target domain is not available, we use the clustering information of the source domain to assign pseudo labels to the target domain samples, and then according to the source domain data prior knowledge guides those positive features to maximum the inter-class distance between different classes and mimimum the intra-class distance.
Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges
This paper starts with the current developments of federated learning and its applications in various fields.
Policy Optimization in Zero-Sum Markov Games: Fictitious Self-Play Provably Attains Nash Equilibria
Specifically, in each iteration, each player infers the policy of the opponent implicitly via policy evaluation and improves its current policy by taking the smoothed best-response via a proximal policy optimization (PPO) step.
Peer-Assisted Robotic Learning: A Data-Driven Collaborative Learning Approach for Cloud Robotic Systems
Both data and models are shared by robots to the cloud after semantic computing and training locally.
A Real-time Contribution Measurement Method for Participants in Federated Learning
However, there is a lack of a sufficiently reasonable contribution measurement mechanism to distribute the reward for each agent.
FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning
Furthermore, we developed federated learning open-source software based on FedCM.
Experiments of Federated Learning for COVID-19 Chest X-ray Images
However, for the protection and respect of the privacy of patients, the hospital's specific medical-related data did not allow leakage and sharing without permission.
COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining
With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions.
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
Compared with transfer learning and meta-learning, FIL is more suitable to be deployed in cloud robotic systems.
Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy
Proximal policy optimization and trust region policy optimization (PPO and TRPO) with actor and critic parametrized by neural networks achieve significant empirical success in deep reinforcement learning.
Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
The experimental results demonstrate that FIL is capable of increasing imitation learning of local robots in cloud robotic systems.
Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy
Proximal policy optimization and trust region policy optimization (PPO and TRPO) with actor and critic parametrized by neural networks achieve significant empirical success in deep reinforcement learning.
Traffic Flow Combination Forecasting Method Based on Improved LSTM and ARIMA
In this paper, we define the traffic data time singularity ratio in the dropout module and propose a combination prediction method based on the improved long short-term memory neural network and time series autoregressive integrated moving average model (SDLSTM-ARIMA), which is derived from the Recurrent Neural Networks (RNN) model.
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL).
Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy
Such an error reduction phenomenon is somewhat surprising as the estimated surrogate policy is less accurate than the given historical policy.
