A Multiobjective Collaborative Deep Reinforcement Learning Algorithm for Jumping Optimization of Bipedal Robot
Bibliographic record
Abstract
Due to the nonlinearity and underactuation of bipedal robots, developing efficient jumping strategies remains challenging. To address this, a multiobjective collaborative deep reinforcement learning algorithm based on the actor‐critic framework is presented. Initially, two deep deterministic policy gradient (DDPG) networks are established for training the jumping motion, each focusing on different objectives and collaboratively learning the optimal jumping policy. Following this, a recovery experience replay mechanism, predicated on dynamic time warping, is integrated into the DDPG to enhance sample utilization efficiency. Concurrently, a timely adjustment unit is incorporated, which works in tandem with the training frequency to improve the convergence accuracy of the algorithm. Additionally, a Markov decision process is designed to manage the complexity and parameter uncertainty in the dynamic model of the bipedal robot. Finally, the proposed method is validated on a PyBullet platform. The results show that the method outperforms baseline methods by improving learning speed and enabling robust jumps with greater height and distance.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".