Imitation learning has long been an approach to alleviate the tractability issues that arise in reinforcement learning. However, most literature makes several assumptions such as access to the expert's actions, availability of many expert demonstrations, and injection of task-specific domain knowledge into the learning process. We propose reinforced inverse dynamics modeling (RIDM), a method of combining reinforcement learning and imitation from observation (IfO) to perform imitation using a single expert demonstration, with no access to the expert's actions, and with little task-specific domain knowledge. Given only a single set of the expert's raw states, such as joint angles in a robot control task, at each time-step, we learn an inverse dynamics model to produce the necessary low-level actions, such as torques, to transition from one state to the next such that the reward from the environment is maximized. We demonstrate that RIDM outperforms other techniques when we apply the same constraints on the other methods on six domains of the MuJoCo simulator and for two different robot soccer tasks for two experts from the RoboCup 3D simulation league on the SimSpark simulator.
There has been much success of using covariance matrix adaptation evolution strategy (CMA-ES) @cite_0 for derivative-free optimization in reinforcement learning. Salimans2017EvolutionSA ( Salimans2017EvolutionSA ) have noted the scalability of evolutionary algorithms for reinforcement learning tasks. We have also seen much success of applying CMA-ES to skill learning in robot soccer . For example, for walking, AAAI12-MacAlpine ( AAAI12-MacAlpine ) have used CMA-ES to learn an omnidirectional walk engine, which is currently among the best in the RoboCup 3D simulation league. For kicking, LNAI14-Depinet ( LNAI14-Depinet ) develop a method called KSOBI (keyframe sampling, optimization, and behavior integration) that uses CMA-ES to learn a @math m long distance kick.
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Hyperparameters of deep neural networks are often optimized by grid search, random search or Bayesian optimization. As an alternative, we propose to use the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which is known for its state-of-the-art performance in derivative-free optimization. CMA-ES has some useful invariance properties and is friendly to parallel evaluations of solutions. We provide a toy example comparing CMA-ES and state-of-the-art Bayesian optimization algorithms for tuning the hyperparameters of a convolutional neural network for the MNIST dataset on 30 GPUs in parallel.