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The aim of this paper is to study the reward based policy exploration problemin a supervised learning approach and enable robots to form complex movementtrajectories in challenging reward settings and search spaces. For this, theexperience of the robot, which can be bootstrapped from demonstratedtrajectories, is used to train a novel Neural Processes-based deep network thatsamples from its latent space and generates the required trajectories givendesired rewards. Our framework can generate progressively improved trajectoriesby sampling them from high reward landscapes, increasing the reward gradually.Variational inference is used to create a stochastic latent space to samplevarying trajectories in generating population of trajectories given targetrewards. We benefit from Evolutionary Strategies and propose a novel crossoveroperation, which is applied in the self-organized latent space of theindividual policies, allowing blending of the individuals that might addressdifferent factors in the reward function. Using a number of tasks that requiresequential reaching to multiple points or passing through gaps between objects,we showed that our method provides stable learning progress and significantsample efficiency compared to a number of state-of-the-art roboticreinforcement learning methods. Finally, we show the real-world suitability ofour method through real robot execution involving obstacle avoidance.

Reward Conditioned Neural Movement Primitives for Population Based Variational Policy Optimization