Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search (1610.00673v1)

Abstract: In principle, reinforcement learning and policy search methods can enable robots to learn highly complex and general skills that may allow them to function amid the complexity and diversity of the real world. However, training a policy that generalizes well across a wide range of real-world conditions requires far greater quantity and diversity of experience than is practical to collect with a single robot. Fortunately, it is possible for multiple robots to share their experience with one another, and thereby, learn a policy collectively. In this work, we explore distributed and asynchronous policy learning as a means to achieve generalization and improved training times on challenging, real-world manipulation tasks. We propose a distributed and asynchronous version of Guided Policy Search and use it to demonstrate collective policy learning on a vision-based door opening task using four robots. We show that it achieves better generalization, utilization, and training times than the single robot alternative.

• The paper introduces an Asynchronous Distributed Guided Policy Search (ADGPS) that enables multiple robots to share experiences and learn collectively, significantly cutting training times.
• It leverages a distributed asynchronous architecture where local robots perform independent rollouts and optimizations without waiting for synchronous updates.
• Experimental evaluations in simulation and real-world door-opening tasks demonstrate improved generalization and robust performance across diverse conditions.

Overview of "Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search"

The paper entitled "Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search" introduces an innovative approach to collective robot learning by leveraging distributed asynchronous methodologies. The authors propose a variant of Guided Policy Search (GPS) that allows multiple robots to collaboratively learn a policy by sharing their experiences, thereby addressing key challenges in robotic reinforcement learning (RL): generalization in a varied real-world environment and improving training times.

The work capitalizes on the potential of distributed and asynchronous interactions to improve upon conventional RL methods, which generally suffer from high sample complexity and limited generalization. By employing a distributed and asynchronous GPS, the paper demonstrates that robotic systems can achieve superior performance on tasks such as door opening under variable conditions.

Key Contributions and Methodological Insights

The primary contribution of this research lies in the development of an Asynchronous Distributed Guided Policy Search (ADGPS) method. The authors identify two main challenges in collective policy learning: utilization, which involves maximizing the effectiveness of robots in collecting experience, and synchronization, which entails effectively integrating individual robot experiences into a cohesive policy. The proposed solution uses a distributed asynchronous system to execute policy learning, addressing both challenges simultaneously.

1. Asynchronous Distributed Training: The introduction of a distributed, asynchronous architecture fundamentally alters the GPS approach. This variant contains several local robots collecting data and local policy optimizations, and a parameter server consolidating global policy updates. Such a setup increases efficiency as robots perform rollouts and optimize local policies without waiting for synchronous global policy updates.
2. Cross-Robot Experience Sharing: By effectively pooling experiences from multiple robots, the system allows for gathering a richer and more diverse dataset for training. This facilitates the development of policies that better generalize to varying conditions in the real world.
3. Practical Evaluation: The system is validated both in simulation and on a complex real-world door opening task. The experimental results indicate significant improvements in performance metrics, including generalization and a reduction in training times, when compared to conventional GPS methods.

Experimental Evaluation

To evaluate their approach, the authors tested it in both simulated environments and with real robotic hardware on a door opening task. The simulated experiments demonstrated that increasing the number of robots (workers) directly reduced training times due to the distributed nature of data collection. In real-world evaluations, the robots successfully opened doors with variance in handle design and orientation, demonstrating the generalization capabilities of the trained visuomotor policies.

One critical success observed was that, unlike standard GPS which may falter with unseen variations, the policy trained with ADGPS maintained high success rates across multiple robots, showing adaptability to new camera positions and door handles. This occurs due in part to the expressive power of the utilized neural network architecture, which was pre-trained on visual features and optimized using asynchronous updates from diverse robot experiences.

Implications and Future Work

The implications of this paper are significant for robot learning and broader AI research. The distributed asynchronous framework enables faster adaptation and generalization across varying task settings, recognizing the viability of collective robotic RL in real-world applications. By addressing the constraints of single-robot learning and synchronization, this paper paves the way for more scalable robotic systems capable of learning diverse tasks collectively.

Future work may focus on extending these methods to non-homogeneous robot systems, where the robots differ in more than just their operational experiences and environments. Moreover, exploring combinations of shared global policy components with individualized local adaptations could unlock new avenues for heterogeneous collaborative robot teams.

In summary, this paper effectively bridges the gap between theoretical reinforcement learning and its practical, scalable deployment, moving towards a future where robots learn collaboratively from shared experience, significantly enhancing their autonomy and operational scope.