Network architecture enforced symmetry
Bibliographic record
Abstract
Deep reinforcement learning has only been recently used to control characters with continuous action space and high degrees of freedom.The locomotion produced by policies that are generated from the direct application of deep reinforcement learning are seldom symmetric.As such a direct application does not provide a mechanism to impart symmetry.One of the ways to generate more human-like locomotion is to introduce a reward to mimic motion-capture data and the other way is to introduce symmetry constraints.This thesis proposes a novel method that is hard-constrained to be state-action space symmetric and enforces temporal-state symmetry while penalizing for energy usage.This mechanism can generate a variety of natural looking gait patterns that capture the correct level of symmetry for the given task in an automated fashion.The proposed method is robust to learning a variety of desired gaits even on rugged terrain.This level of diversity is greater than an existing state-of-the-art work that uses deep reinforcement learning for symmetric locomotion.In quantitative comparisons, our method generates policies that are comparable in visual appeal and show lower variance in evaluation metrics compared to the state-of-the-art.i 5.5 Evaluation metrics of policies generated for walking gait on flat terrain, using methods specified in the comparative analysis. . . . . . . . . . . . . 5.6Evaluation metrics of policies generated for walking gait on rugged terrain, using methods specified in the comparative analysis. . . . . . . . . . . . .ix 5.7 Evaluation metrics of policies generated for walking gait of the asymmetric character, on flat terrain, using selected methods. . . . . . . . . . . . . . .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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; both teacher heads agree on what is shown here.
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".