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We introduce Synthetic Environments (SEs) and Reward Networks (RNs),represented by neural networks, as proxy environment models for trainingReinforcement Learning (RL) agents. We show that an agent, after being trainedexclusively on the SE, is able to solve the corresponding real environment.While an SE acts as a full proxy to a real environment by learning about itsstate dynamics and rewards, an RN is a partial proxy that learns to augment orreplace rewards. We use bi-level optimization to evolve SEs and RNs: the innerloop trains the RL agent, and the outer loop trains the parameters of the SE /RN via an evolution strategy. We evaluate our proposed new concept on a broadrange of RL algorithms and classic control environments. In a one-to-onecomparison, learning an SE proxy requires more interactions with the realenvironment than training agents only on the real environment. However, oncesuch an SE has been learned, we do not need any interactions with the realenvironment to train new agents. Moreover, the learned SE proxies allow us totrain agents with fewer interactions while maintaining the original taskperformance. Our empirical results suggest that SEs achieve this result bylearning informed representations that bias the agents towards relevant states.Moreover, we find that these proxies are robust against hyperparametervariation and can also transfer to unseen agents.

Learning Synthetic Environments and Reward Networks for Reinforcement Learning