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Found 19 papers, 1 papers with code
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Under the limited bandwidth constraint, a communication protocol is required to generate informative messages.
Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning
Existing value-factorized based Multi-Agent deep Reinforce-ment Learning (MARL) approaches are well-performing invarious multi-agent cooperative environment under thecen-tralized training and decentralized execution(CTDE) scheme, where all agents are trained together by the centralized valuenetwork and each agent execute its policy independently.
Learning Expensive Coordination: An Event-Based Deep RL Approach
The main difficulties of expensive coordination are that i) the leader has to consider the long-term effect and predict the followers' behaviors when assigning bonuses and ii) the complex interactions between followers make the training process hard to converge, especially when the leader's policy changes with time.
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication
Thus, the global policy of the whole page could be sub-optimal.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
MetaInfoNet: Learning Task-Guided Information for Sample Reweighting
Deep neural networks have been shown to easily overfit to biased training data with label noise or class imbalance.
Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Execution
Portfolio management via reinforcement learning is at the forefront of fintech research, which explores how to optimally reallocate a fund into different financial assets over the long term by trial-and-error.
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning
Centralized training with decentralized execution (CTDE) has become an important paradigm in multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Safe Coupled Deep Q-Learning for Recommendation Systems
Firstly, we introduce a novel training scheme with two value functions to maximize the accumulated long-term reward under the safety constraint.
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE).
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Reinforcement Learning for Quantitative Trading
RL's impact is pervasive, recently demonstrating its ability to conquer many challenging QT tasks.
Feature Shapley: A general framework to discovering useful feature interactions
A machine learning system is typically composed of model and data.
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE).
Multi-agent Reinforcement Learning
reinforcement-learning
+3
DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities
Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading.
Attention over Self-attention:Intention-aware Re-ranking with Dynamic Transformer Encoders for Recommendation
We then introduce a dynamic transformer encoder (DTE) to capture user-specific inter-item relationships among item candidates by seamlessly accommodating the learned latent user intentions via IDM.
Off-Beat Multi-Agent Reinforcement Learning
During execution durations, the environment changes are influenced by, but not synchronised with, action execution.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach
To tackle these two limitations, we first reformulate quantitative investment as a multi-task learning problem.
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning
Despite its success, ACL's applicability is limited by (1) the lack of a general student framework for dealing with the varying number of agents across tasks and the sparse reward problem, and (2) the non-stationarity of the teacher's task due to ever-changing student strategies.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective
MOBA games, e. g., Dota2 and Honor of Kings, have been actively used as the testbed for the recent AI research on games, and various AI systems have been developed at the human level so far.
Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Control
To address these challenges, we introduce Synapse, a computer agent featuring three key components: i) state abstraction, which filters out task-irrelevant information from raw states, allowing more exemplars within the limited context, ii) trajectory-as-exemplar prompting, which prompts the LLM with complete trajectories of the abstracted states and actions to improve multi-step decision-making, and iii) exemplar memory, which stores the embeddings of exemplars and retrieves them via similarity search for generalization to novel tasks.
