Reinforcement learning (RL) involves defining the state and action spaces for an agent interacting with an environment. State parametrization defines […]

One-step reinforcement learning bandit is a simple yet effective algorithm for solving sequential decision problems. In this setting, the agent

Iterative learning control (ILC) improves the performance of closed-loop control systems by utilizing feedback to enhance their accuracy over time.

RL Parametrizations: Reinforcement learning (RL) involves designing parametrized policies and value functions to represent the agent’s behavior and estimate the

Stylized Offline Reinforcement Learning combines the principles of reinforcement learning with offline data utilization. It leverages pre-collected data to train

Optimal control dynamic programming is a mathematical approach to finding the optimal control policy for a dynamic system. It involves

Variable state space in reinforcement learning refers to environments where the state space—the set of possible states that an agent

Economic Model Predictive Control (EMPC) is a sophisticated technique that combines economic principles and optimization with Model Predictive Control (MPC).

Reward Booster Reinforcement Learning is a reinforcement learning method that uses a reward booster to encourage the agent to explore

Entities Exhibiting Strong Closeness Scores Describe the concept of closeness scores and explain why a score between 8 and 10

Probability proportional to size (PPS) is a sampling method where the probability of selecting a unit is proportional to its

Applied probability and statistics combine statistical principles with practical applications in various fields. They involve using probability theory and statistical