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Found 9 papers, 1 papers with code
Ordinal Potential-based Player Rating
It was recently observed that Elo ratings fail at preserving transitive relations among strategies and therefore cannot correctly extract the transitive component of a game.
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
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective
One of the most fundamental questions in quantitative finance is the existence of continuous-time diffusion models that fit market prices of a given set of options.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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.
