Expand description
Neural Reinforcement Learning Step Size Control
This module implements cutting-edge reinforcement learning-based adaptive step size control using deep Q-networks (DQN) with advanced features including:
- Dueling network architecture
- Prioritized experience replay
- Multi-step learning
- Meta-learning adaptation
- Multi-objective reward optimization
- Attention mechanisms for feature importance
- Noisy networks for exploration
Structsยง
- Adam
Optimizer State - Convergence
Metrics - DeepQ
Network - Deep Q-Network implementation with dueling architecture
- Experience
- Single experience tuple for training
- Feature
Normalization - Importance
Sampling Config - Multi
Objective Reward Calculator - Multi-objective reward calculator
- Network
Hyperparameters - Network
Weights - Network weights for the deep Q-network
- NeuralRL
Step Controller - Neural reinforcement learning step size controller
- Performance
Baselines - Performance
Metrics - Prioritized
Experience Replay - Prioritized experience replay with importance sampling
- Problem
Characteristics - Problem
State - RLEvaluation
Results - RLPerformance
Analytics - Performance analytics for RL training
- Replay
Buffer Config - Reward
Shaping - Reward
Weights - State
Feature Extractor - State feature extractor for RL agent
- Step
Size Prediction - SumTree
- Training
Configuration - Training configuration for the RL agent
- Training
Result - Training
Statistics