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Found 20 papers, 7 papers with code
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials
Machine Learning (ML) has offered innovative perspectives for accelerating the discovery of new functional materials, leveraging the increasing availability of material databases.
Policy Space Response Oracles: A Survey
Game theory provides a mathematical way to study the interaction between multiple decision makers.
Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness
Limit order books are a fundamental and widespread market mechanism.
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.
Model-based gym environments for limit order book trading
This paper introduces \mbtgym, a Python module that provides a suite of gym environments for training reinforcement learning (RL) agents to solve such model-based trading problems.
Market Making with Scaled Beta Policies
This paper introduces a new representation for the actions of a market maker in an order-driven market.
Trading via Selective Classification
A binary classifier that tries to predict if the price of an asset will increase or decrease naturally gives rise to a trading strategy that follows the prediction and thus always has a position in the market.
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.
Difference Rewards Policy Gradients
Policy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning.
The Complexity of Gradient Descent: CLS = PPAD $\cap$ PLS
We study search problems that can be solved by performing Gradient Descent on a bounded convex polytopal domain and show that this class is equal to the intersection of two well-known classes: PPAD and PLS.
A deep learning approach to identify unhealthy advertisements in street view images
Our project presents a novel implementation for the incidental classification of street view images for identifying unhealthy advertisements, providing a means through which to identify areas that can benefit from tougher advertisement restriction policies for tackling social inequalities.
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.
The Automated Inspection of Opaque Liquid Vaccines
We train 3D-ConvNets to predict the likelihood of 20-frame video samples containing anomalies.
Evolving Indoor Navigational Strategies Using Gated Recurrent Units In NEAT
Simultaneous Localisation and Mapping (SLAM) algorithms are expensive to run on smaller robotic platforms such as Micro-Aerial Vehicles.
Negative Update Intervals in Deep Multi-Agent Reinforcement Learning
For instance, hysteretic Q-learning addresses miscoordination while leaving agents vulnerable towards misleading stochastic rewards.
Beyond Local Nash Equilibria for Adversarial Networks
Save for some special cases, current training methods for Generative Adversarial Networks (GANs) are at best guaranteed to converge to a `local Nash equilibrium` (LNE).
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.
GANGs: Generative Adversarial Network Games
We introduce Generative Adversarial Network Games (GANGs), which explicitly model a finite zero-sum game between a generator ($G$) and classifier ($C$) that use mixed strategies.
Lenient Multi-Agent Deep Reinforcement Learning
We find that LDQN agents are more likely to converge to the optimal policy in a stochastic reward CMOTP compared to standard and scheduled-HDQN agents.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
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