Black-Box Data-efficient Policy Search for Robotics

Abstract: The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that maximizes the expected return given the model and its uncertainties. It is often believed that this optimization can be tractable only if analytical, gradient-based algorithms are used; however, these algorithms require using specific families of reward functions and policies, which greatly limits the flexibility of the overall approach. In this paper, we introduce a novel model-based RL algorithm, called Black-DROPS (Black-box Data-efficient RObot Policy Search) that: (1) does not impose any constraint on the reward function or the policy (they are treated as black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for data-efficient RL in robotics, and (3) is as fast (or faster) than analytical approaches when several cores are available. The key idea is to replace the gradient-based optimization algorithm with a parallel, black-box algorithm that takes into account the model uncertainties. We demonstrate the performance of our new algorithm on two standard control benchmark problems (in simulation) and a low-cost robotic manipulator (with a real robot).

• The paper introduces Black-DROPS, a novel black-box policy search that removes constraints on reward functions and policy design in robotics.
• It achieves comparable data efficiency to state-of-the-art methods like PILCO on tasks such as inverted pendulum and cart-pole, while significantly reducing computation time through parallel processing.
• By explicitly managing model uncertainties with global optimization techniques, Black-DROPS effectively escapes local optima and broadens its applicability across diverse robotic systems.

Black-Box Data-efficient Policy Search for Robotics: An Insightful Overview

The paper "Black-Box Data-efficient Policy Search for Robotics" introduces a novel approach to reinforcement learning (RL) within robotic systems. The authors propose Black-DROPS (Black-box Data-efficient RObot Policy Search), aiming to address some of the inherent limitations in existing model-based RL algorithms, particularly concerning constraints on reward functions and policy representations.

Overview of Black-DROPS

The Black-DROPS algorithm is positioned as a versatile alternative to the prevailing analytical methods used in model-based RL, such as PILCO. It is designed with three core differentiators:

1. Flexibility: Black-DROPS treats reward functions and policies as black-boxes. This removes the constraints that typically require specific families of reward functions or policies, thus broadening its applicability across various robotic tasks.
2. Data Efficiency and Speed: The algorithm maintains comparable data efficiency to state-of-the-art methods and achieves high computational efficiency when multiple processing cores are available. This is achieved by utilizing parallelizable black-box optimization techniques that account for model uncertainties, circumventing the computational intensity often associated with gradient-based optimizers.
3. Robustness Against Local Optima: Through the use of global search techniques like CMA-ES, Black-DROPS can escape local optima more effectively than traditional methods, providing a better exploration of the policy space.

Numerical Results and Claims

The authors validate Black-DROPS through extensive experiments on benchmark control tasks such as the inverted pendulum and cart-pole, alongside a practical application on a low-cost robotic manipulator. Key findings include:

• Performance: The algorithm achieves high-quality policy solutions with similar or fewer episodes of interaction compared to PILCO. For instance, Black-DROPS consistently reached higher rewards in the inverted pendulum task and exhibited less variance.
• Computational Advantages: Deploying Black-DROPS on multi-core systems resulted in substantial speed-ups, outperforming PILCO as the number of cores increased. This highlights the algorithm's potential to leverage modern computing architectures effectively.
• Model Uncertainty Management: A notable insight is that explicit consideration of model uncertainties improves performance in more complex tasks, although simpler dynamics might not necessitate this complexity.

Implications and Future Directions

The implications of this research are significant for the broader field of data-efficient RL in robotics:

• Practical Applicability: By removing constraints on reward functions and policies, Black-DROPS can be applied to a wider range of robotic systems, potentially increasing the adaptability of robots in dynamic environments.
• Computational Scalability: The algorithm's ability to exploit parallel processing resonates with the ongoing advancements in multi-core CPU and GPU architectures, suggesting its relevance will grow as computational resources evolve.
• Evolution of Model-Based RL: This work contributes to a growing body of research that explores alternatives to purely analytical approaches, highlighting the potential of black-box optimization in handling model uncertainties effectively.

Looking forward, future developments could focus on further reducing computational requirements, possibly by exploring alternative probabilistic models like Bayesian neural networks. Additionally, more extensive real-world deployments could validate Black-DROPS comprehensively across diverse robotic applications.

Overall, this paper presents an innovative, flexible, and efficient approach to policy search in robotics, showcasing the viability of black-box optimization techniques in overcoming the limitations of traditional analytical methods.