Addressing Different Goal Selection Strategies In Hindsight Experience Replay With Actor-Critic Methods For Robotic Hand Manipulation
Bibliographic record
Abstract
One of the most challenging problems in reinforcement learning is dealing with minimal rewards obtained from an environment. We present a combined technique of Twin Delayed Deep Deterministic Policy Gradient known as TD3, an off-policy Reinforcement Learning algorithm with Hindsight Experience Replay (HER). This combined technique allows for sampleefficient learning from sparse and binary rewards and avoids the need for complicated reward engineering. We use the challenge of moving things with a robotic arm to illustrate our methodology. We specifically tested six different tasks: pushing, sliding, picking up and placing in the Fetch environment, as well as manipulating a block, an egg, or a pen with our hands. We solely use binary rewards every time to indicate whether or not a task has been performed. In a comparative study, we primarily concentrate on the impact of various goal selection strategies of HER replay butter on both DDPG and TD3. We discovered that HER was crucial in enabling training in these demanding situations.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".