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Found 9 papers, 4 papers with code
Collaborative AI Teaming in Unknown Environments via Active Goal Deduction
With the advancements of artificial intelligence (AI), we're seeing more scenarios that require AI to work closely with other agents, whose goals and strategies might not be known beforehand.
Real-time Network Intrusion Detection via Decision Transformers
Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e. g., network intrusion detection from a sequence of arriving packets.
Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning
Offline reinforcement learning aims to utilize datasets of previously gathered environment-action interaction records to learn a policy without access to the real environment.
MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization
To this end, we propose MAC-PO, which formulates optimal prioritized experience replay for multi-agent problems as a regret minimization over the sampling weights of transitions.
ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning
Such an optimization problem can be relaxed and solved using the Lagrangian multiplier method to obtain the close-form optimal projection weights.
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has witnessed significant progress with the development of value function factorization methods.
On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning
In this paper, we present a unifying framework for heterogeneous FL algorithms with {\em arbitrary} adaptive online model pruning and provide a general convergence analysis.
Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients
To this end, we present LSF-SAC, a novel framework that features a variational inference-based information-sharing mechanism as extra state information to assist individual agents in the value function factorization.
PT-VTON: an Image-Based Virtual Try-On Network with Progressive Pose Attention Transfer
The virtual try-on system has gained great attention due to its potential to give customers a realistic, personalized product presentation in virtualized settings.
