Composable Deep Reinforcement Learning for Robotic Manipulation (1803.06773v1)

Abstract: Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, model-free methods are known to perform poorly when the interaction time with the environment is limited, as is the case for most real-world robotic tasks. In this paper, we study how maximum entropy policies trained using soft Q-learning can be applied to real-world robotic manipulation. The application of this method to real-world manipulation is facilitated by two important features of soft Q-learning. First, soft Q-learning can learn multimodal exploration strategies by learning policies represented by expressive energy-based models. Second, we show that policies learned with soft Q-learning can be composed to create new policies, and that the optimality of the resulting policy can be bounded in terms of the divergence between the composed policies. This compositionality provides an especially valuable tool for real-world manipulation, where constructing new policies by composing existing skills can provide a large gain in efficiency over training from scratch. Our experimental evaluation demonstrates that soft Q-learning is substantially more sample efficient than prior model-free deep reinforcement learning methods, and that compositionality can be performed for both simulated and real-world tasks.

• The paper introduces soft Q-learning to achieve sample-efficient learning in robotic manipulation through maximum entropy policies.
• It demonstrates the composability of learned policies by combining Q-functions, enabling modular and scalable skill acquisition.
• Experimental results validate rapid adaptation, with a Sawyer robot autonomously stacking Lego blocks in just two hours.

Composable Deep Reinforcement Learning for Robotic Manipulation

The paper "Composable Deep Reinforcement Learning for Robotic Manipulation" addresses the challenges in deploying model-free deep reinforcement learning (RL) methods, specifically soft Q-learning, for real-world robotic manipulation tasks. The focus on sample efficiency and compositionality of learned policies distinguishes this research as it seeks to bridge the gap between theoretical advancements and practical applications in robotic reinforcement learning.

Key Contributions

1. Maximum Entropy Policies: The paper emphasizes the use of maximum entropy reinforcement learning, where policies maximize both reward and entropy. This approach provides inherent exploration benefits and allows for handling multimodal action spaces, contributing to robust learning in deterministic environments.
2. Soft Q-Learning (SQL): The integration of soft Q-learning enables the learning of expressive energy-based policies, allowing for more sample-efficient training than traditional model-free RL methods like DDPG and NAF. The empirical results indicate SQL’s superior performance in both simulated and physical robotic environments.
3. Policy Compositionality: A significant theoretical contribution is the framework for composing learned policies. The authors demonstrate that by combining the Q-functions of individual policies, new compound policies can be constructed. A theoretical bound on the optimality of the composed policy with respect to the divergence of the constituent policies is provided, suggesting robust compositional capabilities.
4. Experimental Validation: Experiments conducted demonstrate the practical efficiency of SQL in capturing complex manipulation skills quickly on both simulated platforms and real-world robots. The Sawyer robot, for instance, learned to stack Lego blocks autonomously within two hours, showcasing sample efficiency and policy robustness to perturbations.

Implications and Future Directions

The research explores the automated construction of complex robotic policies, a crucial advancement for scaling robotic capabilities in unstructured environments. The composability of policies opens pathways for modular skill acquisition, where robotic systems can build upon existing skills rather than learning from scratch, significantly reducing the learning curve for multifaceted tasks.

Practical Implications:

• Real-World Deployment: The findings suggest SQL as a viable candidate for real-world robotic applications, particularly where sample efficiency and adaptability to variations in task specifications are necessary.
• Complex Task Decomposition: By enabling the combination of simpler policies, more intricate tasks can be broken down into manageable sub-tasks, enhancing the reusability of learned skills.

Theoretical Implications:

• Entropy-Driven Exploration: The paper validates the role of maximum entropy in reinforcing exploration and minimizing the sample complexity of RL algorithms.
• Composability Framework: The bounded divergence model offers a novel lens through which the optimization and reliability of composed policies can be assessed.

Conclusion

The paper provides compelling evidence for the use of soft Q-learning as a means to achieve more efficient and scalable reinforcement learning for robotic manipulation. By focusing on sample efficiency and policy compositionality, this research not only advances the theoretical underpinnings of RL but also enhances its practical applicability. Looking forward, deeper exploration into the compositionality framework and entropy-driven policy development will likely further solidify SQL’s position in advanced robotic learning systems.