Reinforcement learning hyperparameters. However, conventional wisdom from continual...
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Reinforcement learning hyperparameters. However, conventional wisdom from continual learning suggests that naive Sequential Fine-Tuning (Seq. Jan 1, 2026 · Hyperparameters play a critical role in enabling reinforcement learning (RL) agents to achieve high performance, yet their optimization remains computationally demanding. Jan 23, 2024 · Finding good hyperparameters for reinforcement learning (RL) is a notoriously difficult task. This paper proposes a framework to derive interpretable and transferable Rule Extraction (RE) controllers from Deep Reinforcement Learning (DRL) contr… 1 day ago · After this phase of supervised learning, the procedure switches to an RL-based, adaptive phase, where a reinforcement learning agent tunes the hyperparameters of the model, such as regularization strength, learning rate, and signal window sizes by incorporating a reward function considering Lipschitz compliance, signal fidelity, and Detailed hyperparameters are provided in Table 4. The reinforcement learning algorithms DDPG, TD3, and SAC are implemented using the Stable-Baselines3 (SB3) library and compared to assess their performance, and stability in continuous control tasks, the implementation is available in Burgaud (2025). Mastering these parameters is essential for designing RL systems that perform effectively. This coursework focuses on the Cartpole problem, a fundamental challenge in control theory and reinforcement learning. Eimer et al. In this paper, we show that hyperparameter choices in RL can significantly This example shows how to tune the hyperparameters of a reinforcement learning agent using Bayesian optimization. FT) leads to catastrophic forgetting, necessitating complex CRL strategies.
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