Effect of immediate reward function on the performance of reinforcement learning-based energy management system
Bibliographic record
Abstract
The performance of reinforcement learning-based energy management system for a pure hybrid electric vehicle critically depends on the articulation of immediate reward function. The current brief systematically unveils the fundamental reliance of reinforcement learning-based agent’s performance on the articulation of immediate reward function. Third generation Toyota hybrid system is chosen as the electrified powertrain for formulating the energy management problem. An asynchronous advantage actor-critic-based reinforcement learning framework is chosen as the control strategy for the energy management system of the aforementioned powertrain. The chosen powertrain architecture offers two degrees-of-freedom, i.e., engine speed and engine torque. Since reinforcement learning agent is solely responsible for controlling these two variables over a given drive cycle without any tactical controllers, reinforcement learning-based agent not only has to find the near-optimal trajectory for the control variables, but should also consider the feasibility criteria for practical operation. Since reinforcement learning agent chooses the control variables randomly without any feasibility check, immediate reward function should be articulated in such a way so that the agent is discouraged to choose any control variable resulting in infeasible powertrain operation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".