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Found 10 papers, 2 papers with code
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.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
Reductive MDPs: A Perspective Beyond Temporal Horizons
Solving general Markov decision processes (MDPs) is a computationally hard problem.
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.
Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification
This paper presents an end-to-end system for fact extraction and verification using textual and tabular evidence, the performance of which we demonstrate on the FEVEROUS dataset.
Counterfactual Explanations for Arbitrary Regression Models
We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models.
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.
A Natural Actor-Critic Algorithm with Downside Risk Constraints
We prove that this proxy for the lower partial moment is a contraction, and provide intuition into the stability of the algorithm by variance decomposition.
Robust Market Making via Adversarial Reinforcement Learning
We show that adversarial reinforcement learning (ARL) can be used to produce market marking agents that are robust to adversarial and adaptively-chosen market conditions.
Market Making via Reinforcement Learning
Market making is a fundamental trading problem in which an agent provides liquidity by continually offering to buy and sell a security.
