Reward unidentifiability in reinforcement learning (RL) occurs when an agent's learned reward function doesn't align with the true objective, leading to unintended or harmful behaviors. This is especially problematic in safety-critical applications like autonomous systems or AI alignment, where accurate reward modeling is essential. The challenge worsens when rewards are derived from human preferences or demonstrations, as ambiguity in feedback can skew the learned function. While adversarial training has improved robustness in areas like computer vision, its application to reward unidentifiability in RL remains underexplored.

A New Approach to Reward Alignment

One way to tackle this issue is by training a reward-predictive model alongside an RL agent and using adversarial techniques to refine its accuracy. The process could involve:

1. Initial Training: An RL agent and a reward-predictive model (e.g., a neural network) learn to estimate rewards from states and actions.
2. Comparison: The model's predictions are compared to actual rewards using divergence metrics like KL divergence.
3. Adversarial Training: Inputs that maximize disagreement between predictions and true rewards are generated, and the model is retrained to reconcile these discrepancies.
4. Extension: Instead of perturbing inputs, pseudo-inputs (adversarial activations) could trick the reward model, refining it through backpropagation.

This method could benefit RL researchers seeking robust algorithms, AI safety practitioners requiring better-aligned systems, and industries like robotics or gaming that rely on reliable RL agents.

How It Compares to Existing Methods

Unlike existing approaches, this idea actively tests and corrects misalignments in the reward function:

• Inverse Reinforcement Learning (IRL) learns rewards from expert demonstrations but lacks adversarial robustness checks.
• Adversarial Imitation Learning (e.g., GAIL) matches agent behavior to experts but doesn't focus on reward function alignment.
• Bayesian RL models uncertainty passively, whereas this method proactively identifies and resolves ambiguities.

Validating and Scaling the Idea

Starting small could help validate the concept:

1. MVP: Test in simple environments like CartPole or GridWorld with a basic adversarial training loop.
2. Scaling: Expand to complex environments (e.g., MuJoCo) and human-in-the-loop reward signals.
3. Extension: Only after core validation, explore pseudo-input generation for deeper refinement.

Potential challenges include computational costs (mitigated by starting with low-dimensional activations) and training instability (handled via conservative adversarial updates). Success could be measured through improved reward prediction accuracy, safer agent behavior, and better generalization under perturbations.