1,950 research outputs found
Policy gradient in Lipschitz Markov Decision Processes
This paper is about the exploitation of Lipschitz continuity properties for Markov Decision Processes to safely speed up policy-gradient algorithms. Starting from assumptions about the Lipschitz continuity of the state-transition model, the reward function, and the policies considered in the learning process, we show that both the expected return of a policy and its gradient are Lipschitz continuous w.r.t. policy parameters. By leveraging such properties, we define policy-parameter updates that guarantee a performance improvement at each iteration. The proposed methods are empirically evaluated and compared to other related approaches using different configurations of three popular control scenarios: the linear quadratic regulator, the mass-spring-damper system and the ship-steering control
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
Algorithms for CVaR Optimization in MDPs
In many sequential decision-making problems we may want to manage risk by
minimizing some measure of variability in costs in addition to minimizing a
standard criterion. Conditional value-at-risk (CVaR) is a relatively new risk
measure that addresses some of the shortcomings of the well-known
variance-related risk measures, and because of its computational efficiencies
has gained popularity in finance and operations research. In this paper, we
consider the mean-CVaR optimization problem in MDPs. We first derive a formula
for computing the gradient of this risk-sensitive objective function. We then
devise policy gradient and actor-critic algorithms that each uses a specific
method to estimate this gradient and updates the policy parameters in the
descent direction. We establish the convergence of our algorithms to locally
risk-sensitive optimal policies. Finally, we demonstrate the usefulness of our
algorithms in an optimal stopping problem.Comment: Submitted to NIPS 1
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