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Smoothing Policies and Safe Policy Gradients
Policy gradient algorithms are among the best candidates for the much
anticipated application of reinforcement learning to real-world control tasks,
such as the ones arising in robotics. However, the trial-and-error nature of
these methods introduces safety issues whenever the learning phase itself must
be performed on a physical system. In this paper, we address a specific safety
formulation, where danger is encoded in the reward signal and the learning
agent is constrained to never worsen its performance. By studying actor-only
policy gradient from a stochastic optimization perspective, we establish
improvement guarantees for a wide class of parametric policies, generalizing
existing results on Gaussian policies. This, together with novel upper bounds
on the variance of policy gradient estimators, allows to identify those
meta-parameter schedules that guarantee monotonic improvement with high
probability. The two key meta-parameters are the step size of the parameter
updates and the batch size of the gradient estimators. By a joint, adaptive
selection of these meta-parameters, we obtain a safe policy gradient algorithm
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