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Should I Run Offline Reinforcement Learning?

Should I Run Offline Reinforcement Learning?

Artificial Intelligence & Machine Learning

DOCTR-L stands for Distributional Offline Continuous-Time Reinforcement Learning. Offline Reinforcement Learning (RL) is arguably the most prevalent application of reinforcement learning in practical settings. Although offline RL is more common than its online counterpart, it presents greater challenges. In offline RL, a learning agent cannot explore its environment to optimize policies. However, must rely solely on a fixed dataset collected from the actions of other agents, who may have pursued different objectives.

This limitation aligns offline RL with certain aspects of supervised or unsupervised learning.

However, unlike these learning paradigms, offline RL does not provide exposure to an optimal policy. Or actions that reflect such a policy. Consequently, offline RL involves a unique form of data inference or learning.

In a recent paper published in Neural Computing and Applications, I demonstrated that with a continuous-time formulation, risk-sensitive or distributional RL employing stochastic policies can be incorporated into a ‘soft HJB equation’—a nuanced adaptation of the classical Hamilton-Jacobi-Bellman equation.

On the left, we have the value function V(x), and on the right, the corresponding optimal policy π∗(x). The value function graph shows the cost or reward at each state x, while the optimal policy graph indicates the best action to take in each state to minimize the cost or maximize the reward, in accordance with the HJB equation. ​

Moreover, the ‘Deep DOCTR-L’ algorithm, introduced in this paper, utilizes a deep neural network to approximate and learn the optimal solution of the soft HJB equation from offline data. Unlike traditional methods that depend on value iteration or policy iteration, Deep DOCTR-L directly translates high-dimensional offline data into an optimal policy through a process akin to supervised learning. Additionally, the distributional RL framework provides a measurable method for assessing the quality of the derived policies in terms of expected returns and the uncertainties associated with their values.

Read Dr. Igor Halperin’s full paper:

Distributional offline continuous-time reinforcement learning with neural physics-informed PDEs (SciPhy RL for DOCTR-L) | Neural Computing and Applications (springer.com)

[2104.01040] Distributional Offline Continuous-Time Reinforcement Learning with Neural Physics-Informed PDEs (SciPhy RL for DOCTR-L) (arxiv.org)

Should I Run Offline Reinforcement Learning?