Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting

Yuwei FuDi WuBenoit Boulet

article2022AAAI74 citations

Proposes a reinforcement learning framework that dynamically assigns ensemble weights to base models over time, effectively adapting forecasts to non-stationary distributions across real-world time series benchmarks.

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Accurate forecasting of time series data is critical for operational planning, energy management, inventory control, and weather prediction. Real-world temporal data, such as renewable energy generation and customer demand, is inherently non-stationary with shifting dynamics over time. While combining predictions from multiple models via ensemble learning is a proven strategy, finding optimal, dynamic combination weights remains difficult. Static weights or fixed heuristics often underperform because individual forecasting models excel under different data conditions.

The article introduces and evaluates the Reinforcement Learning based Model Combination (RLMC) framework. The core objective is to formulate ensemble weight determination as a sequential decision-making problem that dynamically allocates continuous weights to diverse base forecasting models based on observed time series data and historical performance.

The authors designed a reinforcement learning agent using deep deterministic policy gradients, combining deep convolutional feature extraction from raw time series with embeddings of past model performance. To tackle training instability and avoid overfitting to locally dominant models, the approach incorporates pre-training on classification targets, directed exploration around single-model assignments, and a secondary memory buffer for challenging cases. The framework was evaluated across four standard real-world benchmarks: electricity transformer temperatures (ETT), multi-year climate observations, Global Energy Forecasting Competition power loads (GEFCOM), and daily economic panel series (M4-Daily), using standard mean absolute error and percentage error metrics.

The evaluation revealed several key findings. First, RLMC consistently achieved superior forecasting accuracy across all four datasets, outperforming established heuristic, meta-learning, and alternative machine learning baselines. Second, traditional ensemble methods and earlier learning techniques often failed to beat the single best individual model due to severe overfitting on imbalanced training distributions, an issue RLMC successfully resolved. Third, ablation analysis confirmed that specialized exploration strategies were the single most critical factor for performance gains, followed by the secondary buffer and pre-training steps.

These results demonstrate that dynamic model selection powered by reinforcement learning improves operational forecasting without requiring the manual engineering of domain-specific rules. By reliably combining multiple specialized forecasters, organizations can mitigate the operational risk and cost of sudden forecast failures in fluctuating environments. For practitioners and decision-makers, implementing RLMC offers an automated pathway to upgrade forecasting pipelines using existing statistical and machine learning model libraries.

A primary limitation noted is the deterministic nature of the control policy, which may constrain adaptability when base model superiority is extremely skewed. Organizations considering adoption should conduct pilot testing on their specific domain workflows and explore pairing the framework with unsupervised feature representation methods to ensure robustness across diverse operational conditions.

McGill University communication communication systems deep learning dynamic model combination time series forecasting reinforcement learning

Fu et al (2022).pdf

Paper: FFORMA: Feature-based forecast model averaging, Pablo Montero-Manso et al. (2020). This work introduces feature-based meta-learning to dynamically determine ensemble combination weights for time series forecasting, establishing the foundation for learning dynamic combination policies on temporal data.
Paper: Mining concept-drifting data streams using ensemble classifiers, Haixun Wang et al. (2003). It provides foundational principles on dynamically weighting ensemble models over streaming data to adapt effectively to non-stationary concept drift.
Paper: Time-series forecasting with deep learning: a survey, Bryan Lim et al. (2020). This survey provides essential background on deep learning architectures and hybrid ensembling strategies applied across time series forecasting problems.
Paper: Ensemble deep learning: A review, M. A. Ganaie et al. (2021). It comprehensively reviews decision fusion, dynamic weighting mechanisms, and ensemble designs that motivate learning-based model combinations.
Paper: Neural Network Ensembles, Cross Validation, and Active Learning, Anders Krogh et al. (1994). It introduces foundational theory on measuring model ambiguity and calculating optimal ensemble combination weights.
Paper: Deep Reinforcement Learning: An Overview, Yuxi Li (2017). This overview provides core principles of deep reinforcement learning and sequential decision-making frameworks leveraged by RL-based weighting policies.
Paper: Learning under Concept Drift: A Review, Jie Lu et al. (2019). It delivers key concepts on detecting, understanding, and adapting to non-stationary concept drift in streaming time series data.
Paper: Stacked regressions, LEO BREIMAN (2004). This seminal paper introduces stacked regression frameworks for combining multiple predictors using constrained optimization.

Paper: Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting, Yong Liu et al. (2022). This paper advances forecasting under non-stationarity by designing de-stationary attention mechanisms within deep architectures.
Paper: iTransformer: Inverted Transformers Are Effective for Time Series Forecasting, Yong Liu et al. (2023). It introduces inverted transformer architectures for multivariate forecasting that can serve as state-of-the-art base models in dynamic ensemble pipelines.
Paper: Unified Training of Universal Time Series Forecasting Transformers, Gerald Woo et al. (2024). It explores universal zero-shot time series foundation architectures, extending beyond custom ensemble combination to generalized pre-trained representations.
Paper: Sundial: A Family of Highly Capable Time Series Foundation Models, Yong Liu 0007 et al. (2025). It develops continuous flow-matching foundation models to generate probabilistic time series forecasts across diverse non-stationary domains.

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Abstract

Time series data appears in many real-world fields such as energy, transportation, communication systems. Accurate modelling and forecasting of time series data can be of significant importance to improve the efficiency of these systems. Extensive research efforts have been taken for time series problems. Different types of approaches, including both statistical-based methods and machine learning-based methods, have been investigated. Among these methods, ensemble learning has shown to be effective and robust. However, it is still an open question that how we should determine weights for base models in the ensemble. Sub-optimal weights may prevent the final model from reaching its full potential. To deal with this challenge, we propose a reinforcement learning (RL) based model combination (RLMC) framework for determining model weights in an ensemble for time series forecasting tasks. By formulating model selection as a sequential decision-making problem, RLMC learns a deterministic policy to output dynamic model weights for non-stationary time series data. RLMC further leverages deep learning to learn hidden features from raw time series data to adapt fast to the changing data distribution. Extensive experiments on multiple real-world datasets have been implemented to showcase the effectiveness of the proposed method.

Introduction
Background
Time Series Forecasting
Reinforcement Learning
Related Work
Methods
MDP Setting for Model Combination Problem
Insights from An RL Perspective
Model Combination with DDPG
RL Based Model Combination (RLMC)
Strategies for Efficient Exploration
Algorithm 1: RL-based Model Combination (RLMC)
Experiments
Datasets
Experimental Details
Base Models
Results and Analysis
Conclusion
References

Knowls

Knowl 1 — Markov Decision Process Formulation for Dynamic Model Combination
definition
In the Reinforcement Learning based Model Combination (RLMC) framework, dynamic weighting of $N$ base forecasting models across time is formalized as a Markov Decision Process (MDP) defined by the tuple $\mathcal{M} = \langle \mathcal{S}, \mathcal{A}, \mathcal{P}, r, \gamma \rangle$ :
- State Space $\mathcal{S}$ : The state at discrete timestep $t$ is $s_t = (\mathcal{X}^t, \mathcal{L}^t)$ , where $\mathcal{X}^t = \{x_1^t, \dots, x_T^t\} \in \mathbb{R}^{T \times d_s}$ represents the observed sequence history of length $T$ and feature dimension $d_s$ , and $\mathcal{L}^t = \{L_{t-1}^1, \dots, L_{t-1}^N\} \in \mathbb{R}^N$ represents the historical error metrics of the $N$ base models at timestep $t-1$ .
- Action Space $\mathcal{A}$ : An action $a_t = (w_t^1, \dots, w_t^N) \in \mathbb{R}^N$ is a continuous weight vector on the unit simplex, satisfying $\sum_{i=1}^N w_t^i = 1$ and $w_t^i \in [0, 1]$ . The ensemble prediction is the linear combination $\hat{y}_t = \sum_{i=1}^N w_t^i \hat{y}_t^i$ , where $\hat{y}_t^i$ is the prediction of base model $i$ .
- Transition Dynamics $\mathcal{P}$ : The transition function $\mathcal{P}(s_{t+1} \mid s_t, a_t) = \mathcal{P}(s_{t+1} \mid s_t)$ is decoupled from the action $a_t$ , as model weights do not alter underlying time series transitions.
- Reward Function $r(s, a)$ : The reward $r_t = r(s_t, a_t)$ measures the forecasting accuracy and rank of the ensemble prediction at timestep $t$ .
- Discount Factor $\gamma \in [0, 1]$ : Weight assigned to future forecasting performance ( $\gamma = 0$ for single-step prediction).
The objective is to learn a policy $\pi_\phi(a \mid s)$ maximizing expected return over the state distribution $\mu$ :

$J(\pi) = \mathbb{E}_{s \sim \mu} [V^\pi(s)]$

where:

$V^\pi(s) = \mathbb{E}\left[ \sum_{t=0}^L \gamma^t r(s_t, a_t) \,\middle|\, s_0 = s, a_t \sim \pi_\phi(a_t \mid s_t) \right]$

for trajectory horizon length $L$ .
Knowl 2 — Theoretical Properties of Environment Decoupling and Sparse Optimal Actions
theoretical result
Analyzing time series dynamic model combination from a reinforcement learning perspective yields two structural insights:
1. Model-Based Exploration via Decoupled Dynamics: Because state transitions do not depend on the combination action ( $\mathcal{P}(s_{t+1} \mid s_t, a_t) = \mathcal{P}(s_{t+1} \mid s_t)$ ), treating transitions on historical training data as deterministic places the problem in a model-based setting where transition dynamics and the analytic reward function $r(s_t, a_t)$ are both known. This allows synthetic generation of transitions $(s_t, a_t, r_t, s_{t+1})$ without online interaction.
2. Explosive Log-Probabilities in Stochastic Policy Gradients: When selecting among $N$ base models, the optimal policy is often nearly deterministic, concentrating probability mass on the single best model for a given regime. In stochastic policy gradient methods optimizing:
$\hat{g} = \hat{\mathbb{E}}_t \left[ \nabla_\phi \log \pi_\phi(a_t \mid s_t) \hat{A}_t \right]$

where $\hat{A}_t$ is an advantage estimator, the estimated probabilities $\pi_\phi(a_t \mid s_t)$ for sub-optimal actions approach zero. As a result, $|\log \pi_\phi(a_t \mid s_t)| \to \infty$ , causing numeric explosion and gradient instability. This motivates using deterministic continuous actor-critic methods (such as DDPG) instead of stochastic policy gradient algorithms.
Knowl 3 — RLMC Normalized Mixture Reward Function
equation
In the RLMC framework, the reward $r_t = r(s_t, a_t)$ combines continuous symmetric Mean Absolute Percentage Error (sMAPE) and base model rank performance:

$r_t = \alpha R_t^{sMAPE} + R_t^{rank}$

where $\alpha$ is a balancing hyperparameter. To ensure scale invariance across diverse datasets, both terms are normalized to $[-1, +1]$ :

$R_t^{sMAPE} = 1 - 2 \cdot \frac{\tau(\delta_t)}{9}$

$R_t^{rank} = 1 - 2 \cdot \frac{r_t^p}{N - 1}$

In these equations:
- $\delta_t = \frac{200}{H} \sum_{i=1}^H \frac{|y_{t+i} - \hat{y}_{t+i}|}{|y_{t+i}| + |\hat{y}_{t+i}|}$ is the sMAPE error of the ensemble forecast $\hat{y}_t = \sum_{j=1}^N w_t^j \hat{y}_t^j$ relative to ground truth $y_t$ over forecast horizon $H$ .
- $\tau(\delta_t) \in \{0, 1, \dots, 9\}$ is the decile bin index of $\delta_t$ relative to the empirical base model prediction errors on the training set.
- $r_t^p \in \{0, 1, \dots, N - 1\}$ is the zero-based rank of the ensemble prediction error compared against the individual prediction errors of all $N$ base models at timestep $t$ ( $0$ being best).
Knowl 4 — Actor-Critic Neural Network Architecture in RLMC
model/method
The RLMC actor $\pi_\phi(s)$ and critic $Q_\theta(s, a)$ extract representations from composite states $s_t = (\mathcal{X}^t, \mathcal{L}^t)$ :
- Time Series Encoder: Both networks use Dilated Causal Convolutional Neural Networks (Dilated CNNs) to map raw historical series $\mathcal{X}^t \in \mathbb{R}^{T \times d_s}$ into a temporal latent embedding without future information leakage.
- Loss/Performance Encoder: The historical loss vector $\mathcal{L}^t = \{L_{t-1}^1, \dots, L_{t-1}^N\} \in \mathbb{R}^N$ from timestep $t-1$ is passed through a rank embedding table to produce a model-performance embedding.
- Actor Output: The time series and model loss embeddings are concatenated and passed through fully connected layers to a softmax activation:
$a_t = \pi_\phi(s_t) = \text{softmax}(z_t) \in \mathbb{R}^N$

producing combination weights satisfying $w_t^i \ge 0$ and $\sum_{i=1}^N w_t^i = 1$ .
- Critic Structure: The critic network $Q_\theta(s_t, a_t)$ applies a Dilated CNN encoder to $s_t$ , concatenates the state representation with action vector $a_t$ , and projects to a scalar Q-value through dense linear layers.
Knowl 5 — Sparsity Exploration, Classification Pre-Training, and Dual Buffering
model/method
To improve exploration efficiency on the probability simplex and mitigate policy overfitting to globally dominant base models, RLMC employs three strategies:
1. Actor Pre-training via Multi-Class Classification: Before RL optimization, the actor network $\pi_\phi(s)$ is pre-trained using cross-entropy loss on training data $\mathcal{D}_{train}$ , where the target class for state $s_t$ is the index of the base model that achieved the minimum forecasting error on that instance.
2. Sparsity-Biased Vertex Exploration: To explore simplex extremes where individual models dominate, exploratory actions $a$ are generated by adding random noise $\epsilon \in \mathbb{R}^N$ to a randomly chosen standard basis one-hot vector $e_i \in \mathbb{R}^N$ (with $(e_i)_i = 1$ and $(e_i)_{j \ne i} = 0$ ):
$a = \frac{|e_i + \epsilon|}{\|e_i + \epsilon\|_2}$

where $|\cdot|$ is element-wise absolute value and $\|\cdot\|_2$ normalizes the vector. 3. Dual Replay Buffer for Hard Samples: To prevent the agent from collapsing into outputting fixed weights for globally strong base models, transitions $(s, a, r, s')$ with rewards below a threshold $\hat{r}_t$ (set to $-1$ ) are stored in an auxiliary replay buffer $\mathcal{B}'$ in addition to the standard buffer $\mathcal{B}$ . Training batches are sampled jointly from both buffers.
Knowl 6 — Deep Deterministic Policy Gradient Optimization for Model Weights
model/method
RLMC trains a deterministic actor $\pi_\phi(s)$ and critic $Q_\theta(s, a)$ via Deep Deterministic Policy Gradient (DDPG).

The critic parameters $\theta$ minimize the Mean-Squared Bellman Error over mini-batches from replay buffers $\mathcal{D}$ :

$\min_\theta \mathbb{E}_{(s, a, r, s') \sim \mathcal{D}} \left[ \left( y - Q_\theta(s, a) \right)^2 \right]$

where target $y$ is computed using target critic $Q_{\theta'}$ and target actor $\pi_{\phi'}$ :

$y = r + \gamma Q_{\theta'}\left(s', \pi_{\phi'}(s')\right)$

The actor parameters $\phi$ are updated by gradient ascent on the expected Q-value:

$\max_\phi \mathbb{E}_{s \sim \mathcal{D}} \left[ Q_\theta\left(s, \pi_\phi(s)\right) \right]$

Target networks are updated softly using Polyak parameter $\tau$ :

$\theta' \leftarrow \tau \theta + (1 - \tau) \theta', \quad \phi' \leftarrow \tau \phi + (1 - \tau) \phi'$
Knowl 7 — RLMC Training Algorithm
algorithm
The RLMC training procedure integrates actor classification pre-training, vertex-biased exploration, dual replay buffer sampling, and DDPG updates.
Input: Pre-trained base models $M = \langle M_1, \dots, M_N \rangle$ , training set $\mathcal{D}_{train}$ , total training steps $T_{steps}$ , exploration parameter $\epsilon$ , trajectory horizon $H$ , reward threshold $\hat{r}_t$ , update frequency $d$ , Polyak update parameter $\tau$ .
Output: Learned actor policy $\pi_\phi$ and critic $Q_\theta$ .
Initialize critic $Q_\theta$ and actor $\pi_\phi$ with random weights $\theta, \phi$ .
Initialize target networks $\theta' \leftarrow \theta$ and $\phi' \leftarrow \phi$ .
Initialize replay buffer $\mathcal{B}$ and extra hard-sample buffer $\mathcal{B}'$ .
Pre-train $\pi_\phi(s)$ on $\mathcal{D}_{train}$ as a multi-class classifier using cross-entropy loss against best-performing base model indices.
for $i = 1$ to $T_{steps}$ do
    Randomly select starting index $j$ from $\mathcal{D}_{train}$ .
    Construct initial state $s$ from series $\mathcal{X}^j$ and base model performance history $\mathcal{L}^j$ .
    for step $k = 1$ to $H$ do
        Select action $a$ with $\epsilon$ -greedy sparsity-biased strategy:
            With probability $\epsilon$ , sample basis vector $e_m$ and set $a = \frac{|e_m + \epsilon|}{\|e_m + \epsilon\|_2}$ ; otherwise set $a = \pi_\phi(s)$ .
        Compute ensemble forecast $\hat{y} = \sum_{m=1}^N w^m \hat{y}^m$ .
        Compute mixture reward $r = \alpha R^{sMAPE} + R^{rank}$ .
        Construct next state $s'$ from $\mathcal{X}^{j+k}$ and $\mathcal{L}^{j+k}$ .
        Store transition $(s, a, r, s')$ in replay buffer $\mathcal{B}$ .
        if $r < \hat{r}_t$ then
            Store transition $(s, a, r, s')$ in extra buffer $\mathcal{B}'$ .
        end if
         $s \leftarrow s'$
    end for
    if $i \bmod d == 0$ then
        Sample mini-batch of transitions $(s, a, r, s')$ jointly from $\mathcal{B}$ and $\mathcal{B}'$ .
        Compute target $y = r + \gamma Q_{\theta'}(s', \pi_{\phi'}(s'))$ .
        Update critic $Q_\theta$ by minimizing $(y - Q_\theta(s, a))^2$ .
        Update actor $\pi_\phi$ via gradient ascent on $Q_\theta(s, \pi_\phi(s))$ .
        Update target networks:
             $\theta' \leftarrow \tau \theta + (1 - \tau) \theta'$
             $\phi' \leftarrow \tau \phi + (1 - \tau) \phi'$
    end if
end for
return $\pi_\phi, Q_\theta$

Knowl 8 — Forecasting Benchmark Performance of RLMC Across Datasets

data/table

Forecasting performance of RLMC was evaluated across four datasets: ETT (D1, hourly transformer oil temperature), Climate (D2, hourly weather temperature), GEFCOM (D3, hourly energy load), and M4-Daily (D4, daily panel series). Performance was measured using Mean Absolute Error (MAE, $\sigma_1$ ) and symmetric Mean Absolute Percentage Error (sMAPE in %, $\sigma_2$ ) across 5 experimental runs.

Dataset	Uniform		Single		FFORMS		FFORMA		DMS		M3		RLMC
	$\sigma_1$	$\sigma_2$	$\sigma_1$	$\sigma_2$	$\sigma_1$	$\sigma_2$	$\sigma_1$	$\sigma_2$	$\sigma_1$	$\sigma_2$	$\sigma_1$	$\sigma_2$	$\sigma_1$	$\sigma_2$
D1 (ETT)	5.22	79.83	4.59	73.69	4.89	75.98	5.42	80.77	5.14	78.12	4.63	74.06	4.39	72.11
D2 (Climate)	1.70	26.44	1.67	25.80	1.78	26.65	1.69	25.78	1.70	25.89	1.66	25.66	1.47	25.50
D3 (GEFCOM)	112.65	3.51	89.09	2.75	95.03	2.94	86.93	2.69	90.10	2.79	86.85	2.70	88.25	2.73
D4 (M4)	250.39	3.93	147.61	2.39	183.90	3.12	161.52	2.72	150.36	2.48	148.60	2.45	145.78	2.31

RLMC achieved the lowest MAE and sMAPE on ETT, Climate, and M4, and competitive accuracy on GEFCOM. Neural combination methods (DMS, M3, RLMC) consistently outperformed tree-based meta-learning baselines (FFORMS, FFORMA), demonstrating the efficacy of Dilated CNN time-series feature representations. Standard baselines frequently underperformed the Single Best validation model due to training set overfitting, which RLMC mitigated.

Knowl 9 — Ablation Analysis of RLMC Training and Exploration Components
empirical result
An ablation study on the Climate dataset analyzed the relative contribution of classification pre-training (w/o pret), sparsity-biased exploration (w/o expl), and the auxiliary buffer for low-reward samples (w/o buffer):
- Full RLMC: Achieved an MAE of approximately $1.47$ and an sMAPE of approximately $25.50\%$ .
- Without Sparsity Exploration (w/o expl): Produced the largest degradation, increasing MAE to $\approx 1.71$ and sMAPE to $\approx 26.4\%$ , underperforming the Single Best baseline ( $1.67$ MAE, $25.80\%$ sMAPE). This confirms that vertex-biased exploration is essential to prevent DDPG from settling into sub-optimal interior simplex weights.
- Without Classification Pre-training (w/o pret): MAE deteriorated to $\approx 1.59$ and sMAPE to $\approx 26.0\%$ .
- Without Hard-Sample Buffer (w/o buffer): MAE deteriorated to $\approx 1.56$ and sMAPE to $\approx 25.9\%$ , showing that replaying low-reward transitions prevents the agent from overfitting to training-dominant models.
Knowl 10 — Limitations of Deterministic Policy in Highly Imbalanced Settings
limitation
Because RLMC relies on Deep Deterministic Policy Gradient (DDPG), the policy network $\pi_\phi(s)$ outputs a deterministic point on the simplex action space. In time series datasets with severe class/model imbalance—where a single base model outperforms others on a large majority of training samples—a deterministic actor can suffer from limited exploration flexibility and may prematurely collapse toward selecting that dominant model.

Coverage note — None was omitted; all primary MDP formulations, theoretical insights, reward functions, architectural designs, algorithms, empirical benchmarks, ablations, and stated limitations are included.

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Citation

MLA

Fu, Y., et al. “Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting”. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 6, 2022, pp. 6639–47, https://doi.org/10.1609/AAAI.V36I6.20618.

APA

Fu, Y., Wu, D., & Boulet, B. (2022). Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 6639–6647. https://doi.org/10.1609/AAAI.V36I6.20618

Chicago

Fu, Y., D. Wu, and B. Boulet. 2022. “Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting”. Proceedings of the AAAI Conference on Artificial Intelligence 36 (6): 6639–47. https://doi.org/10.1609/AAAI.V36I6.20618.

Harvard

Fu, Y., Wu, D. and Boulet, B. (2022) “Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting”, Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), pp. 6639–6647. Available at: https://doi.org/10.1609/AAAI.V36I6.20618.

Vancouver

1. Fu Y, Wu D, Boulet B (2022) Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 36:6639–6647

BibTeX

@article{Fu_2022, title={Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting}, volume={36}, ISSN={2159-5399}, url={http://dx.doi.org/10.1609/AAAI.V36I6.20618}, DOI={10.1609/aaai.v36i6.20618}, number={6}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, publisher={Association for the Advancement of Artificial Intelligence (AAAI)}, author={Fu, Yuwei and Wu, Di and Boulet, Benoit}, year={2022}, month=June, pages={6639–6647} }

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Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting

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Table of Contents

Knowls

Knowl 1 — Markov Decision Process Formulation for Dynamic Model Combination

Knowl 2 — Theoretical Properties of Environment Decoupling and Sparse Optimal Actions

Knowl 3 — RLMC Normalized Mixture Reward Function

Knowl 4 — Actor-Critic Neural Network Architecture in RLMC

Knowl 5 — Sparsity Exploration, Classification Pre-Training, and Dual Buffering

Knowl 6 — Deep Deterministic Policy Gradient Optimization for Model Weights

Knowl 7 — RLMC Training Algorithm

Knowl 8 — Forecasting Benchmark Performance of RLMC Across Datasets

Knowl 9 — Ablation Analysis of RLMC Training and Exploration Components

Knowl 10 — Limitations of Deterministic Policy in Highly Imbalanced Settings

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