Agents trained in simulation may make errors in the real world due to mismatches between training and execution environments. These mistakes can be dangerous and difficult to discover because the agent cannot predict them a priori. We propose using oracle feedback to learn a predictive model of these blind spots to reduce costly errors in real-world applications. We focus on blind spots in reinforcement learning (RL) that occur due to incomplete state representation: The agent does not have the appropriate features to represent the true state of the world and thus cannot distinguish among numerous states. We formalize the problem of discovering blind spots in RL as a noisy supervised learning problem with class imbalance. We learn models to predict blind spots in unseen regions of the state space by combining techniques for label aggregation, calibration, and supervised learning. The models take into consideration noise emerging from different forms of oracle feedback, including demonstrations and corrections. We evaluate our approach on two domains and show that it achieves higher predictive performance than baseline methods, and that the learned model can be used to selectively query an oracle at execution time to prevent errors. We also empirically analyze the biases of various feedback types and how they influence the discovery of blind spots.
Anomaly detection @cite_7 is related but not directly applicable, as blind spots are not rare instances. Instead, they are regions of the state space where the training environment does not match the testing environment, and we learn to efficiently identify these regions through oracle feedback.
@cite_7
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Cloud computing resources that are equipped with GPU devices are widely used for applications that require extensive parallelism, such as deep learning. When the demand of cloud computing instance is low, the surplus of resources is provided at a lower price in the form of spot instance by AWS EC2. This paper proposes DeepSpotCloud that utilizes GPU-equipped spot instances to run deep learning tasks in a cost efficient and fault-tolerant way. Thorough analysis about spot instance price history logs reveals that GPU spot instances show more dynamic price change pattern than other general types of cloud computing resources. To deal with the price dynamicity of the GPU spot instance, DeepSpotCloud utilizes instances in different regions across continents as a single resource pool. This paper also proposes a task migration heuristic by utilizing a checkpointing mechanism of existing deep learning analysis platform to conduct fast task migration when a running spot instance is interrupted. Extensive experiments using real AWS services prove that the proposed task migration method is effective even in a WAN environment with limited network bandwidth. Comprehensive simulations by replaying AWS EC2 price history logs reveal that DeepSpotCloud can achieve 13 more cost gain than a state-of-the-art interrupt-driven scheduling policy. The prototype of DeepSpotCloud is implemented using various cloud computing services provided by AWS to serve real deep learning tasks.