A Hybrid Multi-Task Learning Approach for Optimizing Deep Reinforcement Learning Agents
Bibliographic record
Abstract
Driven by recent technological advancements within the field of artificial intelligence (AI), deep learning (DL) has been emerged as a promising representation learning technique across different machine learning (ML) classes, especially within the reinforcement learning (RL) arena. This new direction has given rise to the evolution of a new technological domain named deep reinforcement learning (DRL) that combines the high representational learning capabilities of DL with existing RL methods. Performance optimization achieved by RL-based intelligent agents designed with model-free-based approaches was majorly limited to systems with RL algorithms focused on learning a single task. The aforementioned approach was found to be quite data inefficient, whenever DRL agents needed to interact with more complex, data-rich environments. This is primarily due to the limited applicability of DRL algorithms to many scenarios across related tasks from the same distribution. One of the possible approaches to mitigate this issue is by adopting the method of multi-task learning. The objective of this research paper is to present a hybrid multi-task learning-oriented approach for the optimization of DRL agents operating within different but semantically similar environments with related tasks. The proposed framework will be built with multiple, individual actor-critic models functioning within independent environments and transferring knowledge among themselves through a global network to optimize performance. The empirical results obtained by the hybrid multi-task learning model on OpenAI Gym based Atari 2600 video gaming environment demonstrates that the proposed model enhances the performance of the DRL agent relatively in the range of 15% to 20% margin.
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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 | 0.001 |
| 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".