VALAN is a framework for navigation agents in photo-realistic environments.
problem Developing agents for indoor navigation tasks.
method Deep reinforcement learning with SEED RL architecture.
result VALAN framework can solve a variety of RL problems.
Agent learns from an expert, adapting to constraints in concept learning.
problem Insufficient query selection in active learning for realistic human domains.
method Imitation learning to reason about both internal goals and external constraints.
result Agent outperforms other active learners under most constrained conditions.
WILD-SCAV benchmarks AI in complex 3D FPS environments.
problem Lack of complexity and diversity in RL environments.
method Developed a 3D open-world FPS game environment.
result Demonstrates effectiveness in benchmarking RL algorithms.
INTAGS uses interactive simulation to improve realism in multi-agent systems.
problem Challenges in developing realistic agent-based simulators for multi-agent systems.
method INTAGS introduces a novel metric to evaluate the difference between real and synthetic multi-agent systems, optimizing a stochastic policy in reinforcement learning to adapt to interactive sequential decision-making environments.
result INTAGS generates more realistic market data compared to state-of-the-art approaches.
RL agents optimize order execution in a realistic market simulation.
problem Optimal order execution challenges in a complex market.
method Multi-agent RL in a historical order book simulation.
result RL agents converge to TWAP strategies in some scenarios.
Paper presents a modular RL framework for Forex trading, addressing limitations of prior studies.
problem Challenges in applying RL to Forex trading, including unrealistic environments, simplified rewards, and restricted action spaces.
method Integrates three components: a friction-aware execution engine, a decomposable reward architecture, and a discrete action interface.
result Empirical evaluation shows strong non-monotonic reward interactions and optimal Sharpe ratio with the full reward configuration.
Simitate is a benchmark for evaluating imitation learning approaches.
problem Evaluating the performance of imitation learning algorithms.
method Hybrid dataset with realistic RGB and depth streams, ground truth poses, and pre-trained models.
result Supports the development of imitation learning approaches through reproducible evaluation metrics.
When modelling stock market dynamics, the price formation is often based on an equilbrium mechanism. In real stock exchanges, however, the price formation is goverend by the order book. It is thus interesting to check if the resulting stylized facts of a model with equilibrium pricing change, remain the same or, more g…
Paper introduces WHAM! dataset for realistic speech separation in noisy environments.
problem Real-world speech separation challenges with noise.
method Created WHAM! dataset with real ambient noise, evaluated various speech separation architectures.
result Speech separation architectures show robustness to noise, but performance decreases.
Deep RL for multi-agent autonomous driving in dynamic environments.
problem Adapting to dynamic, multi-agent driving environments.
method Formulated Partially Observable Markov Games (POSG) for multi-agent learning.
result Demonstrated successful training of multi-agent control policies.
AeGAN improves speech clarity in noisy environments.
problem Improving speech recognition in crowded noisy environments.
method Generative adversarial networks (GAN) with a novel architecture.
result The proposed framework outperforms traditional and learning-based methods.
We present the use of the fitted Q iteration in algorithmic trading. We show that the fitted Q iteration helps alleviate the dimension problem that the basic Q-learning algorithm faces in application to trading. Furthermore, we introduce a procedure including model fitting and data simulation to enrich training data as…
Fourier methods fail to accurately approximate option Greeks in realistic market conditions.
problem Failure of Fourier pricing techniques to approximate Greeks in realistic market parameters.
method Used Fourier techniques like Carr-Madan formula, COS method, and Lewis formula to approximate Greeks, which failed in some market conditions.
result Empirically showed that Fourier methods completely fail to approximate Greeks in realistic market environments.
MarS simulates financial markets using generative models.
problem Simulating realistic financial market effects.
method Order-level generative foundation model (LMM) for realistic, interactive, and controllable order generation.
result Strong scalability and robust realism in MarS.
DCGANs generate realistic breast masses for mammography.
problem Lack of labelled data and imbalanced datasets in medical imaging.
method DCGANs trained on mammographic images to generate synthetic masses.
result DCGANs improve lesion detection in mammography by ~0.09 F1 score.
As sensor networks for health monitoring become more prevalent, so will the need to control their usage and consumption of energy. This paper presents a method which leverages the algorithm's performance and energy consumption. By utilising Reinforcement Learning (RL) techniques, we provide an adaptive framework, which…
Robust OPE framework uses human inputs to improve policy evaluation in changing environments.
problem Inaccurate policy evaluations due to shifts in environment properties.
method Adapts OPE methods to shifts on user-inputted covariates, providing more realistic utility estimates.
result Robust OPE framework yields less pessimistic policy evaluations and captures realistic dataset shifts.
AAMDRL uses DRL to manage assets in noisy, changing environments.
problem Learning in noisy, self-adapting environments with sequential data.
method Augmented state information, one-period lag, walk forward analysis.
result AAMDRL outperforms traditional methods in asset management.
SpaCE tackles spatial confounding in scientific studies.
problem Spatial confounding influences treatment and outcome, leading to spurious associations.
method Introduces SpaCE toolkit for benchmark datasets and tools to evaluate causal inference methods.
result Facilitates automated evaluation of machine learning and causal inference models.
ABIDES-MARL uses MARL to study market behavior in a realistic financial simulation.
problem Understanding equilibrium behavior in complex financial market games.
method Combines MARL with a realistic LOB simulation to study market behavior.
result Validated approach by solving an extended Kyle model and showing how execution strategies shape market dynamics.
In this paper we study a new reinforcement learning setting where the environment is non-rewarding, contains several possibly related objects of various controllability, and where an apt agent Bob acts independently, with non-observable intentions. We argue that this setting defines a realistic scenario and we present …
Recent advances in artificial intelligence have been driven by the presence of increasingly realistic and complex simulated environments. However, many of the existing environments provide either unrealistic visuals, inaccurate physics, low task complexity, restricted agent perspective, or a limited capacity for intera…
Develops RL algorithm for lifelong non-stationary environments.
problem Challenges of reinforcement learning in environments with persistent change.
method Formalizes lifelong non-stationarity, uses latent variable models, and leverages online learning and probabilistic inference.
result Substantial improvement in performance over non-reasoning approaches in lifelong non-stationary environments.
Deep RL improves robot navigation in images.
problem Applying deep RL to visual navigation in realistic environments.
method Extended A2C algorithm with auxiliary tasks for segmentation, depth prediction, and target prediction.
result Method outperforms state-of-the-art visual navigation methods.
Behavioral cloning fails due to ignoring causal structure, leading to worse performance.
problem Behavioral cloning's failure to account for causal structure causes worse performance.
method Investigates and proposes interventions to correct causal misidentification.
result Causal misidentification occurs in various domains and can be mitigated.
Proposes a graph neural network for efficient multi-agent routing.
problem Routing multiple agents in complex, sparsely connected graphs with dynamic traffic.
method Value Iteration Network based on graph neural networks for online coordination.
result Significantly outperforms traditional methods in terms of cost and runtime.
This paper explores how environmental properties can simplify reinforcement learning in non-episodic settings.
problem Challenges in reinforcement learning with continuous interaction and sparse delayed rewards.
method Analysis of environment shaping and dynamism properties to simplify learning.
result Properties like environment shaping and dynamism can significantly ease learning in non-episodic, sparse reward settings.
We consider the exploration problem: an agent equipped with a depth sensor must map out a previously unknown environment using as few sensor measurements as possible. We propose an approach based on supervised learning of a greedy algorithm. We provide a bound on the optimality of the greedy algorithm using submodulari…
DIVA generates diverse tasks for complex simulators, enabling adaptive agent training.
problem Lack of diverse training data for complex, open-ended simulators.
method Evolutionary approach using domain randomization and procedural generation.
result Successfully trains adaptive agent behavior in complex simulators.
Robust real-world learning should benefit from both demonstrations and interactions with the environment. Current approaches to learning from demonstration and reward perform supervised learning on expert demonstration data and use reinforcement learning to further improve performance based on the reward received from …
Paper presents method for generating synthetic data to evaluate GPMs in complex environments.
problem Lack of realistic scenarios and datasets to evaluate GPMs in complex environments.
method Agile knowledge representation framework to model requirements and generate synthetic ground truth data.
result Synthetic data can be generated to robustly evaluate GPMs in complex environments.
CopyCAT attacks neural policies by manipulating observations, not states.
problem Adversarial attacks on neural reinforcement learning agents.
method CopyCAT is a targeted attack that lures agents into following outsider policies.
result CopyCAT is effective in a read-only setting where adversaries can only manipulate observations.
We propose a method for downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks. The primary contribution of our paper is an algorithm to enhance the trigger of DL CoMP using online machine learning. We use support vector machine (SVM) classifiers to enhance the user downlink…
Risk-averse reinforcement learning optimizes option hedging.
problem Optimizing option hedging under risk aversion and realistic market conditions.
method Applied Trust Region Volatility Optimization (TRVO) to a vanilla option hedging environment.
result The derived hedging strategy outperforms Black & Scholes and is robust to market variations.
RadioUNet uses deep learning to estimate pathloss in urban environments.
problem Accurate pathloss estimation for urban environments.
method Deep neural networks trained on physical simulation data.
result RadioUNet generates pathloss estimations faster and more accurately than existing methods.
SHIFT simulates realistic financial markets for research and industry.
problem Creating a realistic simulation platform for financial market research.
method Developed a highly realistic financial market simulator with multiple traders and assets.
result Demonstrated that automated agents can produce price processes similar to real markets.
EduGym simplifies reinforcement learning education for students.
problem Students struggle to translate between equations and code in reinforcement learning.
method EduGym provides educational environments and interactive notebooks.
result 86% of students and researchers find EduGym useful for education.
New method converts natural language commands into reward functions for robots.
problem Creating effective reward functions for autonomous machines.
method Language-conditioned reward learning (LC-RL) using inverse reinforcement learning.
result Model learns transferable rewards from natural language commands.
This paper uses probability tensors for efficient path planning in complex scenarios.
problem Efficient path planning in complex environments with obstacles and multiple goals.
method Probability tensors are used to model agent motion and decision-making, incorporating past and future information.
result The model finds solutions in complex scenarios, demonstrating realistic emergent behaviors.
Agent learns to navigate uncertain 3D maps using a hybrid planner.
problem Planning in 3D environments with uncertain topological maps.
method Hierarchical strategy combining graph planner and local policy, data-driven learning with neural network.
result Machine learning can overcome missing information in probabilistic topological maps.
In a voice-controlled smart-home, a controller must respond not only to user's requests but also according to the interaction context. This paper describes Arcades, a system which uses deep reinforcement learning to extract context from a graphical representation of home automation system and to update continuously its…
RL agent learns to place limit orders for trading signals in financial markets.
problem Training an RL agent to execute trading signals in limit order book markets.
method Deep Duelling Double Q-learning with APEX architecture, using synthetic alpha signals.
result RL agent outperforms heuristic trading strategies in inventory management and order placing.
A drone catches another agile drone using competitive reinforcement learning.
problem Intercepting an agile drone with another agile drone.
method Formulated as a Competitive Reinforcement Learning problem, trained with PPO, using a high-fidelity simulation environment.
result Trained policies outperform common heuristic baselines in catch rate, time to catch, and crash rate.
Study examines unsupervised and graph-based methods for anomaly detection in IoBT, outperformed by supervised stacking ensemble.
problem Anomaly detection in adversarial environments of IoBT.
method Unsupervised learning, graph-based methods, ensemble supervised learning, adversarial training.
result Supervised stacking ensemble method outperforms unsupervised and graph-based methods in detecting anomalies.
A new method for robot manipulation tasks using imagined object goals.
problem Learning robot manipulation in sparse reward environments.
method Train objects to reach target positions, then use predictions to create a curriculum of tasks.
result Higher success rates in challenging learning scenarios compared to alternatives.
Capsule Networks improve autonomous navigation in sparse environments.
problem Challenges in reinforcement learning for sparse reward environments.
method Caps-EM pairs CapsNets with Advantage Actor Critic, using fewer parameters.
result Caps-EM achieves significant time improvements and fewer parameters compared to competing methods.
Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off …
New algorithm infers reward function from agent's learning trajectories.
problem Inferring reward function from agent's learning data.
method Gradient-based approach to recover reward function.
result Improved performance compared to state-of-the-art methods.