Assessing Transferability from Simulation to Reality for Reinforcement Learning (1907.04685v2)

Abstract: Learning robot control policies from physics simulations is of great interest to the robotics community as it may render the learning process faster, cheaper, and safer by alleviating the need for expensive real-world experiments. However, the direct transfer of learned behavior from simulation to reality is a major challenge. Optimizing a policy on a slightly faulty simulator can easily lead to the maximization of the Simulation Optimization Bias (SOB). In this case, the optimizer exploits modeling errors of the simulator such that the resulting behavior can potentially damage the robot. We tackle this challenge by applying domain randomization, i.e., randomizing the parameters of the physics simulations during learning. We propose an algorithm called Simulation-based Policy Optimization with Transferability Assessment (SPOTA) which uses an estimator of the SOB to formulate a stopping criterion for training. The introduced estimator quantifies the over-fitting to the set of domains experienced while training. Our experimental results on two different second order nonlinear systems show that the new simulation-based policy search algorithm is able to learn a control policy exclusively from a randomized simulator, which can be applied directly to real systems without any additional training.

Assessing Transferability from Simulation to Reality for Reinforcement Learning: An Expert Overview

The paper "Assessing Transferability from Simulation to Reality for Reinforcement Learning" addresses a crucial challenge in robotics: the direct transfer of robot control policies learned in simulation to real-world environments. Given the complexity and expense of physical experiments, learning in simulation is desirable for its speed and cost-effectiveness. However, transferability issues, primarily due to the reality gap—the discrepancies between simulated and real-world environments—often undermine this process. This paper proposes an innovative approach to quantify and enhance the transferability using domain randomization and a novel stopping criterion based on an optimality gap estimation.

Methodology

The paper introduces Simulation-based Policy Optimization with Transferability Assessment (SPOTA), an algorithm that strategically utilizes domain randomization to improve policy robustness. The essence of this approach is to randomize simulator parameters during training, which broadens the range of system models the policy experiences, thereby enhancing generalization to unmodeled real-world dynamics.

A striking feature of SPOTA is its estimator for the Simulation Optimization Bias (SOB). This estimator quantifies over-fitting to the simulated environments, addressing the tendency to unknowingly exploit inaccuracies in the simulation model, which can result in suboptimal or even damaging policies when transferred to physical systems. The optimality gap, a concept borrowed from the field of stochastic programming, serves as the basis for the estimator. This gap is the difference between the expected performance of the optimal solution and a candidate solution, extended from a finite training domain set to an infinite target domain.

The paper proposes a unique stopping criterion for training based on the Upper Confidence Bound on the Optimality Gap (UCBOG). By bootstrap sampling to estimate the UCBOG, the algorithm confidently assesses when a policy is sufficiently robust for real-world deployment. The policy search concludes when the UCBOG is below a predefined threshold, ensuring a reliable measure of transfer performance.

Experimental Results

Empirically, SPOTA was validated using simulation-to-real experiments on two nonlinear dynamical systems: a ball-balancer and a cart-pole system. The algorithm successfully learned control policies from only the randomized simulator environments that directly transferred to the real systems without additional tuning. Numerical results demonstrated the advantage of SPOTA over traditional PPO and EPOpt (an existing robust RL approach), highlighting a reduced reality gap and improved policy robustness across varying simulator parameters. For instance, the SPOTA policies showed superior performance across domains with differing masses, friction coefficients, and delays.

Implications and Future Directions

This research provides a structured approach to the sim-to-real transfer problem, promoting robustness that is critical for real-world applications. By systematically accounting for the simulation optimization bias, SPOTA not only improves policy transferability but also enriches the theoretical understanding of the reality gap in reinforcement learning contexts.

Looking ahead, further research can explore more efficient ways to parameterize domain distributions or dynamically adapt them, possibly through Bayesian optimization techniques. Additionally, adapting SPOTA to incrementally assimilate real-world data could further bridge the reality gap while minimizing sample complexity. As well, incorporating non-parametric models could better capture the complexities of real environments, opening new avenues for practical and generalized AI deployment.

In conclusion, the paper advances the field by formalizing a measure for evaluating sim-to-real transfer and providing a robust methodology to mitigate associated challenges, offering a promising outlook on the future of adaptive and transferable learning systems in robotics.