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Found 15 papers, 2 papers with code
Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning
Agent-based models (ABMs) have shown promise for modelling various real world phenomena incompatible with traditional equilibrium analysis.
Learning Payment-Free Resource Allocation Mechanisms
We consider the design of mechanisms that allocate limited resources among self-interested agents using neural networks.
O3D: Offline Data-driven Discovery and Distillation for Sequential Decision-Making with Large Language Models
Recent advancements in large language models (LLMs) have exhibited promising performance in solving sequential decision-making problems.
Sequential Fair Resource Allocation under a Markov Decision Process Framework
We propose a new algorithm, SAFFE, that makes fair allocations with respect to the entire demands revealed over the horizon by accounting for expected future demands at each arrival time.
Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning
Knowing this information can help reduce the sample complexity of RL algorithms by masking the inapplicable actions from the policy distribution to only explore actions relevant to finding an optimal policy.
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations
We study a game between liquidity provider and liquidity taker agents interacting in an over-the-counter market, for which the typical example is foreign exchange.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Phantom -- A RL-driven multi-agent framework to model complex systems
Agent based modelling (ABM) is a computational approach to modelling complex systems by specifying the behaviour of autonomous decision-making components or agents in the system and allowing the system dynamics to emerge from their interactions.
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems
Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions.
Mixture of basis for interpretable continual learning with distribution shifts
In this paper we consider settings in which the data distribution(task) shifts abruptly and the timing of these shifts are not known.
Towards a fully RL-based Market Simulator
We present a new financial framework where two families of RL-based agents representing the Liquidity Providers and Liquidity Takers learn simultaneously to satisfy their objective.
Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures
Cheung and Piliouras (2020) recently showed that two variants of the Multiplicative Weights Update method - OMWU and MWU - display opposite convergence properties depending on whether the game is zero-sum or cooperative.
Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPs
In this paper, we address this problem through a factor baseline which exploits independence structure encoded in a novel action-target influence network.
Calibration of Shared Equilibria in General Sum Partially Observable Markov Games
Training multi-agent systems (MAS) to achieve realistic equilibria gives us a useful tool to understand and model real-world systems.
Risk-Sensitive Reinforcement Learning: a Martingale Approach to Reward Uncertainty
We introduce a novel framework to account for sensitivity to rewards uncertainty in sequential decision-making problems.
Reinforcement Learning for Market Making in a Multi-agent Dealer Market
Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk.
