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Found 9 papers, 4 papers with code
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning
In this work, we propose ensemble value functions for multi-agent exploration (EMAX), a general framework to seamlessly extend value-based MARL algorithms with ensembles of value functions.
Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints
In this paper, we introduce a reinforcement learning (RL) framework named \textbf{L}earnable \textbf{I}mpulse \textbf{C}ontrol \textbf{R}einforcement \textbf{A}lgorithm (LICRA), for learning to optimally select both when to act and which actions to take when actions incur costs.
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems
Although there are equilibrium concepts in game theory that take into account risk aversion, they either assume that agents are risk-neutral with respect to the uncertainty caused by the actions of other agents, or they are not guaranteed to exist.
Neural Auto-Curricula in Two-Player Zero-Sum Games
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered as the best response to a mixture over the opponent population.
Multi-agent Reinforcement Learning
Vocal Bursts Valence Prediction
A Game-Theoretic Approach for Improving Generalization Ability of TSP Solvers
In this paper, we introduce a two-player zero-sum framework between a trainable \emph{Solver} and a \emph{Data Generator} to improve the generalization ability of deep learning-based solvers for Traveling Salesman Problem (TSP).
Neural Auto-Curricula
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered as the best response to a mixture over the opponent population.
Modelling Behavioural Diversity for Learning in Open-Ended Games
Promoting behavioural diversity is critical for solving games with non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e. g., Rock-Paper-Scissors).
Online Double Oracle
Solving strategic games with huge action space is a critical yet under-explored topic in economics, operations research and artificial intelligence.
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games.
