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Since only a small region of the chemical space has been explored so far, the amount of data available for certain chemical systems is limited. We overcome the dataset-dependence of generative models for 3D molecular design by formulating this problem as a reinforcement learning task, and propose a symmetry-aware policy that can generate molecular structures unattainable with previous methods. Lastly, we consider the problem of how to efficiently learn robot behaviors across different tasks. A promising direction towards this goal is to generalize locally learned policies over different task contexts. Contextual policy search offers data-efficient learning and generalization by explicitly conditioning the policy on a parametric context space. 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We overcome the dataset-dependence of generative models for 3D molecular design by formulating this problem as a reinforcement learning task, and propose a symmetry-aware policy that can generate molecular structures unattainable with previous methods. Lastly, we consider the problem of how to efficiently learn robot behaviors across different tasks. A promising direction towards this goal is to generalize locally learned policies over different task contexts. Contextual policy search offers data-efficient learning and generalization by explicitly conditioning the policy on a parametric context space. 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However, for many large-scale problems, standard greedy procedures become computationally infeasible. To mitigate this issue, we introduce a scalable Bayesian batch active learning approach that is motivated by approximating the complete data posterior of the model parameters. Second, we address the challenge of automating the design of molecules to accelerate the search for novel drugs and materials. Since only a small region of the chemical space has been explored so far, the amount of data available for certain chemical systems is limited. We overcome the dataset-dependence of generative models for 3D molecular design by formulating this problem as a reinforcement learning task, and propose a symmetry-aware policy that can generate molecular structures unattainable with previous methods. Lastly, we consider the problem of how to efficiently learn robot behaviors across different tasks. 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