Optimization of Deep Reinforcement Learning with Hybrid Multi-Task Learning
Bibliographic record
Abstract
As an outcome of the technological advancements occurred within artificial intelligence (AI) domain in recent times, deep learning (DL) has been established its position as a prominent representation learning method for all forms of machine learning (ML), including the reinforcement learning (RL). Subsequently, leading to the evolution of deep reinforcement learning (DRL) which combines deep learning's high representational learning capabilities with current reinforcement learning methods. Undoubtedly, this new direction has caused a pivotal role towards the performance optimization of intelligent RL systems designed by following model-free based methodology. optimization of the performance achieved with this methodology was majorly restricted to intelligent systems having reinforcement learning algorithms designed to learn single task at a time. Simultaneously, single task-based learning method was observed as quite less efficient in terms of data, especially when such intelligent systems required operate under too complex as well as data rich conditions. The prime reason for this was because of the restricted application of existing methods to wide range of scenarios, and associated tasks from those operating environments. One of the possible approaches to mitigate this issue is by adopting the method of multi-task learning. Objective of this research paper is to present a parallel multi-task learning (PMTL) approach for the optimization of deep reinforcement learning agents operating within two different by semantically similar environments with related tasks. The proposed framework will be built with multiple individual actor-critic models functioning within each environment and transferring the knowledge among themselves through a global network to optimize the performance.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".