Multi-step actor-critic framework for reinforcement learning in continuous control
Bibliographic record
Abstract
Continuous control is an important issue in control theory.It controls an agent to take action in continuous spaces for transiting from one state to another until achieving the desired goal.A useful tool for this issue is the reinforcement learning where an optimal policy is learned for the agent by maximizing the cumulative reward of the state transitions.However, most existing reinforcement learning methods consider only the one-step transition and one-step reward in each state.In this case, it is hard to recognize the information hidden in the sequence of the previous states and accurately estimate the cumulative reward.Therefore, these methods cannot learn the optimal policy both fast and effectively for continuous control.To solve this problem, in this paper, we propose a new framework, called Multi-step Actor-critic Framework (MAF) for reinforcement learning.In MAF, the convolutional deterministic policy is used to learn the information hidden in the sequence of the previous states by convolutional neural networks, and then n-step temporal difference learning is used to accurately estimate the cumulative reward by considering the rewards from n-step states.Based on an effective reinforcement learning method, TD3, the implementation of our MAF is in nTD3.The theoretical analysis and experiment illustrate that our nTD3 can learn the policy not only better but also faster than the existing RL methods for continuous control.
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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 | 0.000 |
| 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".