Discovery of the reward function for embodied reinforcement learning agents
Bibliographic record
Abstract
Reward maximization is a fundamental principle in both the survival and evolution of biological organisms. In particular, in the contexts of cognitive science and embodied agents, reward-driven behavior has been widely regarded as important in terms of governing complex cognitive abilities such as perception, imitation, and learning. Among the frameworks that are aimed at establishing such abilities, reinforcement learning (RL), which leverages reward maximization to facilitate intelligent decision-making in embodied agents, has been proven to be particularly promising. Importantly, the inherent complexity and uncertainty of real-world tasks pose significant challenges when designing effective reward functions for embodied RL agents. Conventional methods typically rely on manually engineered or externally tuned reward signals, and therefore require significant domain expertise, associated with considerable human efforts and a long convergence time; these issues may even trigger mission failure. This work introduces a bilevel optimization framework that discovers optimal reward functions for embodied reinforcement learning agents through a mechanism called regret minimization. The approach accelerates policy optimization and enhances adaptability across diverse tasks. These findings can support the broader adoption of embodied RL agents in the behavioral and computational sciences and neurosciences, thereby paving the way for artificial general intelligence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".