Benchmarking Reinforcement Learning Algorithms on Real-World Robots (1809.07731v1)

Abstract: Through many recent successes in simulation, model-free reinforcement learning has emerged as a promising approach to solving continuous control robotic tasks. The research community is now able to reproduce, analyze and build quickly on these results due to open source implementations of learning algorithms and simulated benchmark tasks. To carry forward these successes to real-world applications, it is crucial to withhold utilizing the unique advantages of simulations that do not transfer to the real world and experiment directly with physical robots. However, reinforcement learning research with physical robots faces substantial resistance due to the lack of benchmark tasks and supporting source code. In this work, we introduce several reinforcement learning tasks with multiple commercially available robots that present varying levels of learning difficulty, setup, and repeatability. On these tasks, we test the learning performance of off-the-shelf implementations of four reinforcement learning algorithms and analyze sensitivity to their hyper-parameters to determine their readiness for applications in various real-world tasks. Our results show that with a careful setup of the task interface and computations, some of these implementations can be readily applicable to physical robots. We find that state-of-the-art learning algorithms are highly sensitive to their hyper-parameters and their relative ordering does not transfer across tasks, indicating the necessity of re-tuning them for each task for best performance. On the other hand, the best hyper-parameter configuration from one task may often result in effective learning on held-out tasks even with different robots, providing a reasonable default. We make the benchmark tasks publicly available to enhance reproducibility in real-world reinforcement learning.

• The paper introduces six distinct RL tasks across three commercial robot platforms, enabling comprehensive performance evaluation in real-world settings.
• The paper benchmarks four state-of-the-art RL algorithms and highlights their significant sensitivity to hyper-parameter tuning.
• The paper provides publicly available benchmarks and source code to spur reproducibility and drive further advances in real-world RL applications.

An Analysis of "Benchmarking Reinforcement Learning Algorithms on Real-World Robots"

The paper, "Benchmarking Reinforcement Learning Algorithms on Real-World Robots," introduces a series of experiments aimed at understanding the applicability of model-free reinforcement learning (RL) approaches on physical robot platforms. The paper acknowledges recent advancements in simulated environments and emphasizes the importance of transitioning these developments to the real world. This necessity arises due to the inherent differences between simulations and real-world robotics, including complexities like system delays and non-deterministic behaviors.

Key Contributions and Methodology

The authors provide several notable contributions:

1. Benchmark Tasks Introduction: The paper introduces six distinct RL tasks utilizing three commercially available robot platforms—UR5 collaborative arm, Dynamixel MX-64AT actuator, and iRobot Create 2. These tasks vary in complexity and context, offering a comprehensive suite for evaluating RL algorithms.
2. Algorithm Evaluation: Four state-of-the-art RL algorithms are evaluated—Trust Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), and Soft Q-learning. The paper benchmarks these algorithms across the defined tasks, analyzing their learning capabilities, sensitivity to hyper-parameters, and overall applicability to real-world scenarios.
3. Public Availability: The research includes the release of benchmark tasks and associated source code, facilitating reproducibility and further research within the RL community.

Experimental Outcomes

One of the primary findings of this research is the extreme sensitivity of RL algorithms to hyper-parameters. This sensitivity suggests that achieving optimal performance in varied tasks requires significant re-tuning. However, the research indicates that TRPO, PPO, and Soft-Q could still achieve effective performance with a broad range of hyper-parameter configurations. This robustness underscores their potential reliability across different robotic platforms.

The results also demonstrate that some of the best-performing configurations on one task may serve as reasonably effective defaults for others, albeit with varying degrees of success. Additionally, the paper illustrates that, while RL solutions can be competitive, they often lag behind well-established scripted solutions unless the task implies uncharted territories like in Create-Docker.

Implications

The paper highlights several implications for reinforcement learning in robotics:

• Practical Application Challenges: The operational challenges encountered during the experiments—such as sensor malfunctions and the physical coupling issues with robots—indicate that RL applications in real-world settings necessitate robust and adaptable algorithms.
• Algorithmic Development: The findings suggest a need for enhancing RL algorithms with greater sample efficiency and the capability to handle faster action cycles. Addressing these computational challenges is crucial for real-time robotics applications.
• Theoretical Direction: The paper also encourages more in-depth exploration of algorithms that can inherently accommodate the stochastic and often unpredictable nature of real-world environments.

Future Directions

The paper suggests multiple areas for future work, including the need to benchmark additional learning algorithms on the proposed tasks and improve existing ones. The push for higher sample efficiency and faster action cycles reflects broader ambitions within the field of robotics and AI to transition more experimental solutions into practical, everyday applications.

In conclusion, this paper provides a crucial step toward understanding the complexities involved in applying reinforcement learning to real-world tasks. By offering a detailed analysis and publicly available benchmarks, it sets a foundational benchmark for future explorations in this dynamic research area.