Improving generalization in reinforcement learning on Atari 2600 games
Bibliographic record
Abstract
Deep Reinforcement Learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a crucial step towards building autonomous systems with a higher-level understanding of the world around them. In particular, deep reinforcement learning has changed the landscape of autonomous agents by achieving superhuman performance on board game Go, a significant milestone in AI research. In this project, we attempt to train a Deep RL network on Demon Attack – an Atari 2600 game and test the model on different game environments to investigate the feasibility of applying Transfer Learning on environments with same action space but slightly different state space. We further extend the project to use established Reinforcement Learning techniques such as DQN, Dueling DQN, and SARSA to examine whether RL agents can be generalized on unfamiliar environments by fine-tuning the hyperparameters. Finally, we borrow classic regularization techniques like 2 regularization and dropout from the world of supervised learning and probe whether these techniques which have received very limited attention in the domain of reinforcement learning are effective in reducing overfitting of Deep RL networks. Deep Networks are expensive to train and complex models take weeks to train using expensive GPUs. We find that the use of the above techniques prevents the network from overfitting on the current environment and gives satisfactory results when tested on slightly different environments thus enabling substantial savings in training time & resources.
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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 | 0.002 | 0.008 |
| 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 | 0.002 |
| 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".