Reinforcement learning has attracted great attention recently, especially policy gradient algorithms, which have been demonstrated on challenging decision making and control tasks. In this paper, we propose an active multi-step TD algorithm with adaptive stepsizes to learn actor and critic. Specifically, our model cons…
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48 results for “Multi-step Algorithms”
Paper adapts ACI for online multi-step time-series forecasting with coverage guarantees.
problem Achieving reliable error bounds in online multi-step time-series forecasting.
method Adaptive conformal inference (ACI) adapted for multi-step forecasting with dynamic significance levels.
result Proposes a multi-step ACI algorithm with finite-sample coverage guarantees for non-exchangeable data.
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?cs.LG
Looped Transformers learn to implement multi-step gradient descent for in-context learning.
problem Understanding the learnability of multi-step algorithms in multi-layer Transformers.
method Training weight-sharing looped Transformers for in-context linear regression, proving gradient dominance condition for convergence.
result Looped Transformers implement multi-step preconditioned gradient descent, converging to global minimizer.
Multi-step temporal difference (TD) learning is an important approach in reinforcement learning, as it unifies one-step TD learning with Monte Carlo methods in a way where intermediate algorithms can outperform either extreme. They address a bias-variance trade off between reliance on current estimates, which could be …
Multi-step greedy policies have been extensively used in model-based reinforcement learning (RL), both when a model of the environment is available (e.g.,~in the game of Go) and when it is learned. In this paper, we explore their benefits in model-free RL, when employed using multi-step dynamic programming algorithms: …
New framework guarantees convergence of multi-step MAML.
problem Convergence of multi-step MAML in nonconvex settings.
method Developed a theoretical framework for two types of MAML objective functions.
result Guaranteed convergence rate and computational complexity for multi-step MAML.
Model-based reinforcement learning is an appealing framework for creating agents that learn, plan, and act in sequential environments. Model-based algorithms typically involve learning a transition model that takes a state and an action and outputs the next state---a one-step model. This model can be composed with itse…
A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learningcs.LG
The paper introduces a multi-step loss function to improve model-based reinforcement learning.
problem Compounding of one-step prediction errors in long trajectories.
method A multi-step objective function combining MSE losses at various future horizons.
result Models trained with the multi-step loss achieve significant improvement in future prediction.
The paper calculates prices for multi-step barrier options under the Black-Scholes model.
problem Calculating prices for multi-step barrier options with varying barriers and time steps.
method Derives a general, explicit expression for option prices using the Black-Scholes model and a multi-step reflection principle.
result Derives a multi-step reflection principle that generalizes the reflection principle of Brownian motion.
The paper addresses Dyna-style RL's value hallucination issue by proposing a new algorithm.
problem Value hallucination in Dyna-style RL due to bootstrapping simulated states.
method Introduces a new Dyna algorithm using predecessor models with multi-step updates.
result Evidence supports the Hallucinated Value Hypothesis (HVH), suggesting predecessor models with multi-step updates are promising.
Recently, a new multi-step temporal learning algorithm, called , unifies -step Tree-Backup (when ) and -step Sarsa (when ) by introducing a sampling parameter . However, similar to other multi-step temporal-difference learning algorithms, needs much memory consumption and computation tim…
Efficiently optimizes expensive functions with multi-step lookahead using one-shot optimization.
problem Optimizing expensive functions with long-term impacts using myopic approaches.
method Formulated as nested optimization problems within a multi-step scenario tree, optimized in one-shot fashion.
result Multi-step expected improvement is computationally tractable and outperforms existing methods.
Paper presents a copula-based method to efficiently generate correlated sample paths from multi-step time series models.
problem Generating realistic correlation structures in multi-step forecast sample paths is expensive and time-consuming.
method Copula-based approach to generate correlated sample paths in one forward pass.
result Improved sample path quality and significant speedup over autoregressive sampling.
Transformers learn multi-step reasoning through gradient descent.
problem Understanding how transformers solve symbolic multi-step reasoning tasks.
method Theoretical analysis of gradient descent dynamics and multi-phase training.
result Trained one-layer transformers can solve both backward and forward reasoning tasks with generalization guarantees.
The paper explores dynamic ensembles for multi-step forecasting.
problem Lack of research on dynamic ensembles for multi-step forecasting.
method Extensive experiments with 3568 time series and an ensemble of 30 multi-output models.
result Dynamic ensembles based on arbitrating and windowing perform best.
A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecastingstat.ML
AEnbMIMOCQR generates robust multi-step ahead prediction intervals for time series data.
problem Generating reliable multi-step ahead prediction intervals for time series data.
method Adaptive ensemble batch multi-input multi-output conformalized quantile regression (AEnbMIMOCQR) based on conformal prediction principles.
result AEnbMIMOCQR provides close to exact coverage and robustness to distribution shifts.
Novel unsupervised feature selection method using multi-step Markov transition probability.
problem Neglected relationships between non-adjacent data points in feature selection.
method MMFS (Multi-step Markov transition probability for Feature Selection) approach, employing positive and negative viewpoints.
result MMFS effectively maintains data structure in unsupervised feature selection.
Multi-step methods such as Retrace() and -step -learning have become a crucial component of modern deep reinforcement learning agents. These methods are often evaluated as a part of bigger architectures and their evaluations rarely include enough samples to draw statistically significant conclusions about thei…
New algorithm learns optimal policy with multi-step lookahead information.
problem Learning optimal policy in reinforcement learning with multi-step lookahead information is NP-hard.
method Adaptive batching policies that process lookahead in state-dependent chunks.
result Order-optimal regret bounds up to a constant factor of lookahead horizon.
Direction Finding Based on Multi-Step Knowledge-Aided Iterative Conjugate Gradient Algorithmseess.SP
In this work, we present direction-of-arrival (DoA) estimation algorithms based on the Krylov subspace that effectively exploit prior knowledge of the signals that impinge on a sensor array. The proposed multi-step knowledge-aided iterative conjugate gradient (CG) (MS-KAI-CG) algorithms perform subtraction of the unwan…
The paper explores dual learning, a technique that improves machine translation and image transformation.
problem Understanding and improving dual learning's effectiveness and conditions.
method Theoretical analysis and algorithmic extension of dual learning.
result Multi-step dual learning boosts performance under mild conditions.
Enhances PMD with lookahead to improve RL performance.
problem Improving RL performance with greedy policies over 1-step.
method Integrates multi-step greedy policies into PMD with lookahead.
result Shows faster convergence rate for -PMD.
Stratify unifies and improves multi-step forecasting strategies.
problem Lack of unified frameworks for multi-step forecasting strategies.
method Proposes Stratify, a parameterized framework for multi-step forecasting.
result Novel strategies in Stratify outperform existing ones in over 84% of experiments.
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger deman…
When environmental interaction is expensive, model-based reinforcement learning offers a solution by planning ahead and avoiding costly mistakes. Model-based agents typically learn a single-step transition model. In this paper, we propose a multi-step model that predicts the outcome of an action sequence with variable …
Paper proposes a dual-level approach for multi-step forecasting of dynamical systems.
problem Accurate multi-step forecasting of time series systems for automatic control and optimization.
method Hybrid input forecasting using LSTM-STMs and physics-informed neural networks (PINNs).
result Hybrid models achieve higher log-likelihood and lower MSE compared to conventional methods.
For the efficient compensation of fiber nonlinearity, one of the guiding principles appears to be: fewer steps are better and more efficient. We challenge this assumption and show that carefully designed multi-step approaches can lead to better performance-complexity trade-offs than their few-step counterparts.
Agents need world models to generalize multi-step tasks.
problem The necessity of world models for flexible, goal-directed behavior.
method Formal analysis and demonstration of the necessity of world models for agents to generalize multi-step tasks.
result World models are necessary for agents to generalize to multi-step goal-directed tasks.
Multi-step ahead forecasting is still an open challenge in time series forecasting. Several approaches that deal with this complex problem have been proposed in the literature but an extensive comparison on a large number of tasks is still missing. This paper aims to fill this gap by reviewing existing strategies for m…
In its simplest form, the traffic flow prediction problem is restricted to predicting a single time-step into the future. Multi-step traffic flow prediction extends this set-up to the case where predicting multiple time-steps into the future based on some finite history is of interest. This problem is significantly mor…
ForecastNet uses a time-variant deep feed-forward neural network for better multi-step-ahead time series forecasting.
problem Time-invariant architectures limit multi-step-ahead forecasting.
method ForecastNet employs a deep feed-forward architecture with time-variant parameters and interleaved outputs.
result ForecastNet outperforms other models on multi-step-ahead time series forecasting tasks.
Diffusion-VAE tackles multi-step stock price prediction with stochastic noise.
problem Challenges in multi-step stock price prediction due to stochasticity and target price sequence.
method Combines hierarchical VAE and diffusion probabilistic techniques for seq2seq stock prediction.
result D-Va model outperforms state-of-the-art solutions in prediction accuracy and variance.
Proposes QDF to improve multi-step time-series forecasting.
problem Ignoring label autocorrelation and unequal task weights in training objectives.
method Quadratic-form weighted training objective and QDF learning algorithm.
result Improves performance of various forecast models, achieving state-of-the-art results.
Accurate time series prediction over long future horizons is challenging and of great interest to both practitioners and academics. As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterat…
A multi-step framework tackles online unsupervised domain adaptation with novel mean-target subspace computation.
problem Online unsupervised domain adaptation with unlabelled target data arriving sequentially.
method Multi-step framework with a novel mean-target subspace computation and temporal coherency consideration.
result Improved performance over previous approaches on four datasets.
Quantile deep learning improves time series prediction accuracy and uncertainty quantification.
problem Uncertainty in multi-step time series prediction.
method Developed a novel quantile regression deep learning framework for multi-step time series prediction.
result Integrating quantile loss function with deep learning provides additional predictions for selected quantiles without loss in accuracy.
BCI provides calibrated prediction intervals for time series forecasts.
problem Calibration of prediction intervals for time series forecasts.
method BCI wraps around any time series forecasting models and optimizes interval lengths using dynamic programming.
result BCI achieves long-term coverage under arbitrary distribution shifts and temporal dependence.
Paper tackles action delays in reinforcement learning, proposing a delay-aware framework.
problem Action delays degrade reinforcement learning performance in real-world systems.
method Formal definition of delay-aware MDP, transformation into standard MDP with augmented states, delay-aware model-based reinforcement learning framework.
result Proposed framework is more efficient in training and transferable between systems with various delay durations.
Proposes a framework to quantify uncertainty in multi-step decision-making by LLMs.
problem Uncertainty quantification in multi-step decision-making scenarios of LLMs.
method A principled, information-theoretic framework decomposing uncertainty into internal and extrinsic components, and proposing UProp for efficient extrinsic uncertainty estimation.
result UProp significantly outperforms existing single-turn UQ baselines in multi-step decision-making benchmarks.
We present a theoretical and experimental investigation of the quantization problem for artificial neural networks. We provide a mathematical definition of quantized neural networks and analyze their approximation capabilities, showing in particular that any Lipschitz-continuous map defined on a hypercube can be unifor…
Temporal-difference (TD) learning is an important field in reinforcement learning. Sarsa and Q-Learning are among the most used TD algorithms. The Q() algorithm (Sutton and Barto (2017)) unifies both. This paper extends the Q() algorithm to an online multi-step algorithm Q() using eligibility traces and int…
We investigate the multi-step prediction of the drivable space, represented by Occupancy Grid Maps (OGMs), for autonomous vehicles. Our motivation is that accurate multi-step prediction of the drivable space can efficiently improve path planning and navigation resulting in safe, comfortable and optimum paths in autonom…
In this work, we propose a subspace-based algorithm for DOA estimation which iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the MSE of the reshaped data covariance matrix is carried out along with c…
This work analyzes CoT prompting methods from a statistical estimation perspective.
problem Improving the effectiveness of LLMs in solving multi-step reasoning problems.
method Introducing a multi-step latent variable model to characterize CoT prompting from a statistical estimation viewpoint.
result The CoT estimator is equivalent to a Bayesian estimator when the pretraining dataset is large.
We present a numerical framework for approximating unknown governing equations using observation data and deep neural networks (DNN). In particular, we propose to use residual network (ResNet) as the basic building block for equation approximation. We demonstrate that the ResNet block can be considered as a one-step me…
JANET improves time series prediction with adaptive uncertainty regions.
problem Time series data's lack of exchangeability and multi-step prediction challenges.
method Proposes JANET, a framework for joint adaptive prediction regions with controlled error rates.
result Demonstrates superior performance in multi-step prediction tasks across diverse datasets.
Proposes a multi-stream RNN model for predicting merchant transactions.
problem Predicting future transaction statistics of merchants.
method Multi-stream RNN model tailored for multivariate time series and multi-step predictions.
result Outperforms existing state-of-the-art methods in merchant transaction predictions.
New method identifies network dynamics and noise structure.
problem Estimating network and disturbance topologies in dynamic systems.
method Extended multi-step Sequential Linear Regression and Weighted Null Space Fitting methods.
result Consistent estimation of dynamic networks with reduced computational burden.