Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we consider the scenario with discrete-continuous hybrid action space. To handle hybrid action space, previous works either approximate the hyb…
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48 results for “hybrid action spaces”
In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-acto…
New algorithms tackle multi-agent problems with hybrid action spaces.
problem Applying deep reinforcement learning to multi-agent problems with discrete-continuous hybrid action spaces.
method Proposed two novel algorithms: Deep MAPQN and Deep MAHHQN, using centralized training and decentralized execution.
result Empirical results show both algorithms significantly outperform existing methods.
Hybrid Policy Optimization tackles reinforcement learning in hybrid spaces, improving performance over PPO.
problem Credit assignment issues and biased gradients in hybrid discrete-continuous action spaces.
method Mixed gradient estimator combining pathwise and score-function gradients, reformulating problems in hybrid form.
result HPO substantially outperforms PPO on inventory control and switched systems, with performance gaps increasing with continuous action dimension.
Proposes hybrid reinforcement learning for both discrete and continuous control problems.
problem Real-world control problems involving both discrete and continuous decision variables.
method Solves hybrid problems by optimizing for discrete and continuous actions simultaneously.
result Efficiently solves hybrid reinforcement learning problems and improves upon expert heuristics.
Hybrid RL learns from expert state sequences without full action data.
problem Learning from expert state sequences without full action data.
method Tensor-based model to infer unobserved actions; hybrid RL objective.
result Hybrid RL outperforms pure RL and tensor-based action inference.
New algorithm fills gaps in offline data for hybrid RL, achieving similar gains without coverage assumptions.
problem Lack of provable benefits in hybrid RL with coverage assumptions.
method Warm-starting optimistic online algorithms with offline data in experience replay buffer.
result Hybrid RL gains similar to offline-only RL without coverage assumptions, demonstrating efficient exploration.
Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Executionq-fin.TR
Hybrid RL method optimizes trading by balancing continuous and discrete actions.
problem Optimal execution in algorithmic trading with continuous-discrete action space.
method Combines continuous and discrete RL agents for better trading decisions.
result Significantly outperforms existing methods in trading efficiency and stability.
Hybridizes CEM and gradient descent for efficient model-predictive control.
problem Efficiently planning optimal action sequences in high-dimensional spaces.
method Interleaves Cross-Entropy Method (CEM) and gradient descent steps.
result Faster convergence and avoidance of local optima compared to CEM.
AQL uses amortized inference to handle high-dimensional action spaces in Q-learning.
problem Difficulty in maximizing over large action spaces in Q-learning.
method Replace expensive maximization over all actions with a maximization over a small subset sampled from a learned proposal distribution.
result AQL outperforms existing methods on continuous control tasks with up to 21 dimensional actions.
Hybrid SAC improves RL for video games with discrete, continuous actions.
problem Improving RL performance in video games with practical constraints.
method Extension of Soft Actor-Critic (SAC) for handling discrete, continuous, and parameterized actions.
result Hybrid SAC successfully solves a high-speed driving task and is competitive on parameterized actions benchmarks.
AI learns to design chemical processes efficiently.
problem Designing efficient chemical processes.
method Hierarchical reinforcement learning and graph neural networks.
result Quick learning in various decision spaces.
A hybrid model combines Q-learning and PID controller for continuous vehicle control.
problem Learning unsatisfactory results with discrete action space in autonomous driving.
method Combining Q-learning and PID controller, using Quadratic Q-function approximation and action network.
result Autonomous vehicle successfully learns smooth and efficient driving behavior.
Non-parabolic Spatial Hybrid Framed Curves and Their Applications in the Spatial Hybrid Number Spacemath.DG
Defines non-parabolic curves in spatial hybrid space with applications.
problem Defining and analyzing non-parabolic spatial hybrid framed curves.
method Definition and proof of existence and uniqueness theorem for non-parabolic spatial hybrid framed curves.
result Existence and uniqueness theorem for non-parabolic spatial hybrid framed curves.
Improved action recognition in live videos with hybrid FR-DL method.
problem High computational costs and lack of temporal information in conventional action recognition.
method Automated selection of representative frames, feature extraction, background subtraction, HOG, deep neural network, LSTM, Softmax-KNN classifier.
result Significant improvement in accuracy and speed compared to state-of-the-art methods.
Hybrid LSTM-PPO optimizes dynamic portfolios with better performance.
problem Dynamic portfolio optimization under non-stationary market conditions.
method Combines LSTM for forecasting and PPO for adaptive portfolio adjustments.
result Hybrid framework outperforms single-model and equal-weight approaches in various metrics.
Mathematical analysis of Riemann surfaces and their moduli spaces using hybrid Laplacians.
problem Analyzing the asymptotics of Arakelov Green functions on Riemann surfaces near boundary of moduli spaces.
method Introducing hybrid Laplacian, solving hybrid Poisson equation, and defining hybrid Green functions.
result Layered description of asymptotics of Arakelov Green functions on Riemann surfaces near boundary of their moduli spaces.
Stochastic domains often involve risk-averse decision makers. While recent work has focused on how to model risk in Markov decision processes using risk measures, it has not addressed the problem of solving large risk-averse formulations. In this paper, we propose and analyze a new method for solving large risk-averse …
Paper introduces hybrid curves moduli space and canonical measures.
problem Asymptotic geometry of Riemann surfaces and their moduli spaces.
method Definition of hybrid curves and canonical measures.
result Continuous variation of canonical measures over hybrid curves moduli space.
A new method combines online and offline learning to tackle contextual bandits with missing action support.
problem Learning optimal policies with logged data when the logging policy has deficient support.
method Hybrid approach using online exploration to exploit supported actions and offline learning to avoid unnecessary explorations.
result Determines an optimal policy with theoretical guarantees using minimal online explorations.
Defines plurisubharmonic metrics on hybrid spaces and proves their canonical extensions.
problem Defining and analyzing plurisubharmonic metrics on hybrid spaces.
method Introduces a class of plurisubharmonic metrics on hybrid spaces and proves their canonical extensions.
result Canonical plurisubharmonic extensions of metrics on hybrid spaces are continuous and can be described in terms of canonical models.
Improved ExO method achieves near-optimal bounds in both stochastic and adversarial settings.
problem Finding optimal exploration strategies in online decision-making with limited feedback.
method Exploration by Optimization with hybrid regularizers for locally observable games.
result Achieved nearly optimal bounds of in stochastic and adversarial environments.
HyBO optimizes hybrid structures using diffusion kernels.
problem Optimizing complex interactions between discrete and continuous variables.
method HyBO uses diffusion kernels over hybrid spaces with additive kernel formulation.
result HyBO significantly outperforms state-of-the-art methods on real-world benchmarks.
Paper proposes efficient inner product approximation for hybrid sparse and dense vectors.
problem Efficient search in hybrid spaces with both sparse and dense components is challenging.
method Proposes a technique to approximate inner product computation in hybrid vectors.
result Achieves over 10x speedup and higher accuracy in search compared to baselines.
As the necessary background to construct from the aspect of Grothendieck's Algebraic Geometry dynamical fermionic D3-branes along the line of Ramond-Neveu-Schwarz superstrings in string theory, three pieces of the building blocks are given in the current notes: (1) basic -algebrogeometric foundations of …
Lecture notes on using non-Archimedean geometry for complex variety degenerations.
problem Complex algebraic variety degenerations with non-Archimedean Berkovich spaces.
method Hybrid spaces and non-Archimedean pluripotential theory.
result Relation between convergence of psh metrics and Monge-Ampere measures in hybrid spaces.
The Bergman measure converges to the Zhang measure on a hybrid space.
problem Proving convergence of Bergman measures to Zhang measure.
method Analyzing convergence on a hybrid space and metrized curve complex.
result Bergman measure converges to Zhang measure on a hybrid space.
Paper addresses hybrid learning with constrained adversaries, achieving optimal performance.
problem Hybrid learning problem with i.i.d. features and adversarial labels.
method Structured adversarial setting, efficient algorithm with ERM oracle.
result Oracle-efficient algorithm with regret scaling with Rademacher complexity.
Study improves U.S. monetary policy forecasting by integrating text and data.
problem Forecasting central bank policy decisions, especially the Fed's rate changes.
method Multi-modal approach combining structured data and unstructured text from Fed communications.
result Hybrid models outperform unimodal baselines, achieving a test AUC of 0.83.
SeER hybrid model improves song recommendations and explains them.
problem Improving song recommendations and explaining them.
method Collaborative filtering and deep learning sequence models on MIDI content.
result Personalized explanations capture user preferences.
Enhanced tracking control for AUVs with improved policy gradient method.
problem Trajectory tracking problem for underactuated AUVs with unknown dynamics and constrained inputs.
method Hybrid actors-critics architecture with multiple actors and critics, Pseudo Q-learning, and deterministic policy gradient.
result High-level tracking control accuracy and stable learning of AUVs.
Paper uses RL to optimize derivative hedging with reduced costs.
problem Optimizing hedging strategies for derivatives with transaction costs.
method Reinforcement learning with two Q-functions, continuous state/action space, hybrid valuation model.
result Optimal hedging reduces mean and variance of hedging costs.
The aim of this paper is to define a chain level refinement of the Batalin-Vilkovisky (BV) algebra structure on the homology of the free loop space of a closed, oriented -manifold. For this purpose, we define a (nonsymmetric) cyclic dg operad which consists of "de Rham chains" of free loops with marked points…
This work improves motion planning for quadcopters by learning and reasoning about controller performance.
problem Improving motion planning for quadcopters with safety margins and execution reliability.
method Introspective learning and reasoning to correct execution bias and improve collision checking.
result Substantial reduction in safety margins for motion actions, leading to safer execution.
Proposes a new metric learning method for image recognition.
problem Improving image recognition performance using learned distance representations.
method Introduces a Generalized Hybrid Metric Loss (GHM-Loss) to learn hybrid proximity features combining geometric and probabilistic spaces.
result Demonstrates superior performance compared to existing methods on public datasets.
Extends Kähler metrics theory to symplectic manifolds with toric actions.
problem Extending invariant Kähler metrics theory to symplectic manifolds with toric actions.
method Using Delzant subspaces and Lagrangian fibrations, establishing a correspondence between metrics and connections.
result Characterizes extremal invariant Kähler metrics as those with scalar curvature on base integral affine manifold.
Hybrid machine learning improves gallstone risk prediction.
problem Complex gallstone disease risk factors and interactions.
method Adaptive LASSO for variable selection, BART for interactions, differential equations for interpretation.
result Enhanced prediction accuracy and actionable insights.
Method improves simulation accuracy by mitigating distribution shift in hybrid systems.
problem Mitigating distribution shift in machine-learning augmented hybrid simulation.
method Tangent-space regularized estimator to control distribution shift.
result Marked improvements in simulation accuracy, especially for systems with high distribution shift.
Hybrid model improves sequential data prediction by combining neural and time series models.
problem Nonlinear prediction in online settings with domain-specific feature engineering issues.
method Joint optimization of LSTM for feature extraction and SARIMAX for time series data using state space representations.
result Significant improvements in real-life competition datasets.
We present a hybrid method for latent information discovery on the data sets containing both text content and connection structure based on constrained low rank approximation. The new method jointly optimizes the Nonnegative Matrix Factorization (NMF) objective function for text clustering and the Symmetric NMF (SymNMF…
Scientific discovery is limited by hypothesis redundancy, and hybrid methods can exploit non-local exploration.
problem Limitation of scientific discovery due to hypothesis redundancy.
method Hybrid discovery systems combining structured local search with LLM-generated non-local proposals.
result Hybrid methods can exploit non-local exploration when three geometric conditions co-occur.
A novel double-space tensor-product RKHS framework for hybrid uncertainty sensitivity analysis.
problem Quantifying the influence of hybrid aleatory and epistemic uncertainties on high-dimensional system responses.
method A novel double-space tensor-product RKHS framework for sensitivity analysis under hybrid uncertainty.
result Concurrent double Möbius inversion orthogonally decomposes global dependence measure into pure aleatory effects, pure epistemic effects, and their interaction contributions.
Hybrid model improves geopolitical conflict forecasting.
problem Forecasting geopolitical events from sparse, bursty data.
method Sparse Temporal Fusion Transformer (TFT) + Variational Nearest Neighbor Gaussian Process (VNNGP).
result Consistently outperforms standalone TFT in long-range horizons.
SJDs unify masked, continuous, and hybrid diffusion models.
problem Unified modeling of diffusion processes.
method Continuous-time Markov processes with token embeddings and hazard rates.
result Unified model recovers masked, continuous, and hybrid diffusion as limits.
A hybrid ML model detects fraudulent transactions with high accuracy.
problem Detecting and preventing fraudulent credit card transactions.
method Intelligent combination of multiple algorithms with Grid search and IHT-LR.
result Achieves impressive accuracy rates of 99.66% for ENS model.
The study uses a multi-armed bandit model to analyze and mitigate hiring discrimination.
problem Hiring discrimination due to insufficient data on worker skill and characteristics.
method Multi-armed bandit model to simulate firms' learning process and policy solutions.
result Temporary affirmative actions effectively alleviate discrimination caused by data insufficiency.
Paper presents J-RFDL for robust DL in compressed space, improving data representation robustness and accuracy.
problem Improving data representation robustness and accuracy in the presence of noise and outliers.
method Joint Robust Factorization and Projective Dictionary Learning (J-RFDL) in a factorized compressed space.
result Delivers superior performance in data representation and classification over state-of-the-art methods.
Hybrid subgroups found in non-arithmetic PU(2,1) lattices.
problem Exploring hybrid subgroups in non-arithmetic PU(2,1) lattices.
method Exploring hybrid subgroups of certain non-arithmetic lattices in PU(2,1). Showing that Mostow's lattices are virtually hybrids and some are hybrids of two non-commensurable arithmetic lattices in PU(1,1).
result Mostow's lattices are virtually hybrids and some are hybrids of two non-commensurable arithmetic lattices in PU(1,1).