Reinforcement studying (RL) algorithms can study abilities to unravel decision-making duties like enjoying video games, enabling robots to select up objects, and even optimizing microchip designs. Nonetheless, operating RL algorithms in the true world requires costly energetic information assortment. Pre-training on various datasets has confirmed to allow data-efficient fine-tuning for particular person downstream duties in pure language processing (NLP) and imaginative and prescient issues. In the identical approach that BERT or GPT-3 fashions present general-purpose initialization for NLP, massive RL–pre-trained fashions may present general-purpose initialization for decision-making. So, we ask the query: Can we allow comparable pre-training to speed up RL strategies and create a general-purpose “spine” for environment friendly RL throughout varied duties?
In “Offline Q-learning on Various Multi-Job Information Each Scales and Generalizes”, to be printed at ICLR 2023, we focus on how we scaled offline RL, which can be utilized to coach worth features on beforehand collected static datasets, to supply such a common pre-training technique. We display that Scaled Q-Studying utilizing a various dataset is enough to study representations that facilitate fast switch to novel duties and quick on-line studying on new variations of a job, bettering considerably over present illustration studying approaches and even Transformer-based strategies that use a lot bigger fashions.
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Scaled Q-learning: Multi-task pre-training with conservative Q-learning
To offer a general-purpose pre-training strategy, offline RL must be scalable, permitting us to pre-train on information throughout completely different duties and make the most of expressive neural community fashions to accumulate highly effective pre-trained backbones, specialised to particular person downstream duties. We based mostly our offline RL pre-training technique on conservative Q-learning (CQL), a easy offline RL technique that mixes commonplace Q-learning updates with a further regularizer that minimizes the worth of unseen actions. With discrete actions, the CQL regularizer is equal to a normal cross-entropy loss, which is a straightforward, one-line modification on commonplace deep Q-learning. A couple of essential design selections made this doable:
- Neural community dimension: We discovered that multi-game Q-learning required massive neural community architectures. Whereas prior strategies usually used comparatively shallow convolutional networks, we discovered that fashions as massive as a ResNet 101 led to vital enhancements over smaller fashions.
- Neural community structure: To study pre-trained backbones which can be helpful for brand spanking new video games, our ultimate structure makes use of a shared neural community spine, with separate 1-layer heads outputting Q-values of every sport. This design avoids interference between the video games throughout pre-training, whereas nonetheless offering sufficient information sharing to study a single shared illustration. Our shared imaginative and prescient spine additionally utilized a realized place embedding (akin to Transformer fashions) to maintain monitor of spatial info within the sport.
- Representational regularization: Current work has noticed that Q-learning tends to undergo from representational collapse points, the place even massive neural networks can fail to study efficient representations. To counteract this concern, we leverage our prior work to normalize the final layer options of the shared a part of the Q-network. Moreover, we utilized a categorical distributional RL loss for Q-learning, which is thought to supply richer representations that enhance downstream job efficiency.
The multi-task Atari benchmark
We consider our strategy for scalable offline RL on a collection of Atari video games, the place the purpose is to coach a single RL agent to play a set of video games utilizing heterogeneous information from low-quality (i.e., suboptimal) gamers, after which use the ensuing community spine to rapidly study new variations in pre-training video games or utterly new video games. Coaching a single coverage that may play many various Atari video games is tough sufficient even with commonplace on-line deep RL strategies, as every sport requires a distinct technique and completely different representations. Within the offline setting, some prior works, similar to multi-game determination transformers, proposed to dispense with RL completely, and as an alternative make the most of conditional imitation studying in an try to scale with massive neural community architectures, similar to transformers. Nonetheless, on this work, we present that this sort of multi-game pre-training might be finished successfully through RL by using CQL together with a couple of cautious design selections, which we describe under.
Scalability on coaching video games
We consider the Scaled Q-Studying technique’s efficiency and scalability utilizing two information compositions: (1) close to optimum information, consisting of all of the coaching information showing in replay buffers of earlier RL runs, and (2) low high quality information, consisting of information from the primary 20% of the trials within the replay buffer (i.e., solely information from extremely suboptimal insurance policies). In our outcomes under, we examine Scaled Q-Studying with an 80-million parameter mannequin to multi-game determination transformers (DT) with both 40-million or 80-million parameter fashions, and a behavioral cloning (imitation studying) baseline (BC). We observe that Scaled Q-Studying is the one strategy that improves over the offline information, attaining about 80% of human normalized efficiency.
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Additional, as proven under, Scaled Q-Studying improves when it comes to efficiency, but it surely additionally enjoys favorable scaling properties: simply as how the efficiency of pre-trained language and imaginative and prescient fashions improves as community sizes get greater, having fun with what is usually referred as “power-law scaling”, we present that the efficiency of Scaled Q-learning enjoys comparable scaling properties. Whereas this can be unsurprising, this sort of scaling has been elusive in RL, with efficiency usually deteriorating with bigger mannequin sizes. This means that Scaled Q-Studying together with the above design selections higher unlocks the power of offline RL to make the most of massive fashions.
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Superb-tuning to new video games and variations
To judge fine-tuning from this offline initialization, we take into account two settings: (1) fine-tuning to a brand new, completely unseen sport with a small quantity of offline information from that sport, comparable to 2M transitions of gameplay, and (2) fine-tuning to a brand new variant of the video games with on-line interplay. The fine-tuning from offline gameplay information is illustrated under. Notice that this situation is mostly extra favorable to imitation-style strategies, Resolution Transformer and behavioral cloning, for the reason that offline information for the brand new video games is of comparatively high-quality. Nonetheless, we see that most often Scaled Q-learning improves over various approaches (80% on common), in addition to devoted illustration studying strategies, similar to MAE or CPC, which solely use the offline information to study visible representations fairly than worth features.
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Within the on-line setting, we see even bigger enhancements from pre-training with Scaled Q-learning. On this case, illustration studying strategies like MAE yield minimal enchancment throughout on-line RL, whereas Scaled Q-Studying can efficiently combine prior information in regards to the pre-training video games to considerably enhance the ultimate rating after 20k on-line interplay steps.
These outcomes display that pre-training generalist worth operate backbones with multi-task offline RL can considerably increase efficiency of RL on downstream duties, each in offline and on-line mode. Notice that these fine-tuning duties are fairly tough: the assorted Atari video games, and even variants of the identical sport, differ considerably in look and dynamics. For instance, the goal blocks in Breakout disappear within the variation of the sport as proven under, making management tough. Nonetheless, the success of Scaled Q-learning, notably as in comparison with visible illustration studying methods, similar to MAE and CPC, means that the mannequin is in reality studying some illustration of the sport dynamics, fairly than merely offering higher visible options.
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| Superb-tuning with on-line RL for variants of the sport Freeway, Hero, and Breakout. The brand new variant utilized in fine-tuning is proven within the backside row of every determine, the unique sport seen in pre-training is within the high row. Superb-tuning from Scaled Q-Studying considerably outperforms MAE (a visible illustration studying technique) and studying from scratch with single-game DQN. |
Conclusion and takeaways
We offered Scaled Q-Studying, a pre-training technique for scaled offline RL that builds on the CQL algorithm, and demonstrated the way it allows environment friendly offline RL for multi-task coaching. This work made preliminary progress in the direction of enabling extra sensible real-world coaching of RL brokers as an alternative choice to expensive and complicated simulation-based pipelines or large-scale experiments. Maybe in the long term, comparable work will result in typically succesful pre-trained RL brokers that develop broadly relevant exploration and interplay abilities from large-scale offline pre-training. Validating these outcomes on a broader vary of extra practical duties, in domains similar to robotics (see some preliminary outcomes) and NLP, is a crucial route for future analysis. Offline RL pre-training has numerous potential, and we count on that we are going to see many advances on this space in future work.
Acknowledgements
This work was finished by Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, and Sergey Levine. Particular because of Sherry Yang, Ofir Nachum, and Kuang-Huei Lee for assist with the multi-game determination transformer codebase for analysis and the multi-game Atari benchmark, and Tom Small for illustrations and animation.
