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Found 13 papers, 2 papers with code
Inducing Cooperation via Learning to reshape rewards in semi-cooperative multi-agent reinforcement learning
Under these semi-cooperative scenarios, popular methods of centralized training with decentralized execution for inducing cooperation and removing the non-stationarity problem do not work well due to lack of a common shared reward as well as inscalability in centralized training.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Curiosity-Driven Multi-Agent Exploration with Mixed Objectives
First, we develop a two-headed curiosity module that is trained to predict the corresponding agent's next observation in the first head and the next joint observation in the second head.
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning
Although these two sources are both important factors for learning robust policies of agents, prior works do not separate them or deal with only a single risk source, which could lead to suboptimal equilibria.
Ranked #1 on
SMAC+
on Off_Near_parallel
Multi-agent Reinforcement Learning
reinforcement-learning
+3
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning
QTRAN is a multi-agent reinforcement learning (MARL) algorithm capable of learning the largest class of joint-action value functions up to date.
Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation
In this paper, we propose an idea of empowering the discriminativeness: Adding a new, artificial class and training the model on the data together with the GAN-generated samples of the new class.
How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction
Hypergraphs provide a natural way of representing group relations, whose complexity motivates an extensive array of prior work to adopt some form of abstraction and simplification of higher-order interactions.
Solving Continual Combinatorial Selection via Deep Reinforcement Learning
IS-MDP decomposes a joint action of selecting K items simultaneously into K iterative selections resulting in the decrease of actions at the expense of an exponential increase of states.
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently.
Ranked #1 on
SMAC+
on Off_Superhard_parallel
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Learning to Schedule Communication in Multi-agent Reinforcement Learning
Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents' interaction, where well-coordinated actions among the agents are crucial to achieve the target goal better at these tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Learning Data Dependency with Communication Cost
In this paper, we consider the problem of recovering a graph that represents the statistical data dependency among nodes for a set of data samples generated by nodes, which provides the basic structure to perform an inference task, such as MAP (maximum a posteriori).
Iterative Bayesian Learning for Crowdsourced Regression
Crowdsourcing platforms emerged as popular venues for purchasing human intelligence at low cost for large volume of tasks.
Adiabatic Persistent Contrastive Divergence Learning
This paper studies the problem of parameter learning in probabilistic graphical models having latent variables, where the standard approach is the expectation maximization algorithm alternating expectation (E) and maximization (M) steps.
Optimal Inference in Crowdsourced Classification via Belief Propagation
Crowdsourcing systems are popular for solving large-scale labelling tasks with low-paid workers.
