Convention

Reinforcement education (RL) agents are capable of making complex decisions in a vibrant environment, yet their behavior is often opaque. When an agent carries a sequence of actions – such as operating insulin to a diabetic patient or controlling the landing of the spacecraft – it is rarely clear how the results have changed under alternative choices. This challenge is especially pronounced in the settings associated with constant action spaces, where decisions are not limited to different options, but the spectrum of real-value parameters is spread. Aims to generate a structure introduced in recent work Adverse explanation In such settings, “what if” offers a structured approach to explore views.

Why the counterpart for RL?

The value of opponent logic in the RL is high in high -to -be, temporarily elaborate results scenarios. The above example explains the case of glucose control in the blood in type -1 diabetes. Here, an RL agent determines the insulin dose at regular intervals in response to the physical signals. In the labeled way The patient’s blood glucose increases in a dangerous range before it eventually decreases, resulting in a moderate total reward. Below this route, three counterfeit options –, And – Shows the possible consequences of a few different insulin dosing decisions. In this, And Gives more accumulated rewards than When Does bad It is worth noting that The original actions achieve the best results with minimal deviations and satisfy the clinically induced barrier: a fixed insulin dose manages when glucose comes down to the predefined threshold.

These examples suggest that counterfacture explanations can help to diagnose and purify the learned behaviors. Instead of treating RL policy as Black B AS Q, this perspective facilitates the identity of marginal adjustments with meaningful effects. It also provides a method for assessing whether domain specialists – clinicians or engineers such as agents’ decisions are set up with established safety and operation criteria.

Competitive policies with minimal deviation

This method creates an opponent explanation as a Optim Ptimization problem, detecting an alternative route that improves performance while staying close to the observation sequence of actions. The proximity is certified by using the corresponding distance metric on continuous action sequences. To solve this, the two delayed Deep Vanda Deterinastic Policy (TD3) is suited to the algorithm prize-shaped method that penalizes major deviations. The resulting counterfacture policy is detrient and designed to create interpretable options from a given initial state.

This formulation involves restricted action settings, where some decisions-or adhere to severe physical condition-dowman-specific policies. This is addressed by creating an old Markov decision process (MDP) separating the uncontrolled parts of the state space while embedding fixed behavior in transition mobility. Then TIM ptimization is selected selectively on the flexible parts of the road.

Instead of building one-oppress for individual examples, the approach learns the general opponent’s policy. This enables a consistent and scalable explanation to pay generation in the distribution of observation behaviors.

Apps: Diabetes Control and Lunar Lander

The empirical evaluation was carried out in two representative domains, with each temporarily involved in constant control of the elaborate environment. The first task includes regulation of glucose using the UVA/Pedova simulator recognized by the FDA, which modeling the body of patients with type -1 diabetes. In this regard, the agent has been tasked with adjusting insulin dose in real time based on glucose trends, carbohydrate intake and other state variables. The goal is to keep glucose in the blood in a safe target category while avoiding hypoglycemic or hyperglycemic events. Counterfactual traitories in this domain explain that policy-compatible changes in insulin administration can achieve improved results.

The second domain lunar lender uses the atmosphere, a standard RL benchmark where the simulated spacecraft must descend directly on the designated pad. The agent must control the thrust from the main and lateral engine to maintain balance and reduce the velocity on the landing. The environment is governed by gravity and velocity, making small control variations potentially effective. In this case, opponent disclosure gives an understanding of how normal control purifies can improve landing stability or use of energy.

In both settings, the approach recognizes an alternative route with a modified performance related to the standard baseline, especially in terms of interpretation and obstruction. Positive competitors – with a high accumulated reward – were found to be more than 50-80% of the test. The learned policy also showed generalization in both single and multi-environment situations.

Limitations and extensive effects

While the framework shows the promise in interpretation and empirical exhibition, it depends on the road-level reward signal with a scattered shape. This design can limit the resolution of response during training, especially in long-room or fine-grained control settings. Nevertheless, the approach contributes to the widespread efforts towards interpretable reinforcement education. It is not important to understand what the agent has chosen in domains where transparency is required – such as healthcare, finance or autonomous systems, but which options can achieve better results. Counterfactual logic provides a way to highlight these possibilities in a structured and policy-awareness.

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Tags Gs: IJCAI, IJCAI2025