OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World (1709.07643v2)

Abstract: While deep reinforcement learning techniques have recently produced considerable achievements on many decision-making problems, their use in robotics has largely been limited to simulated worlds or restricted motions, since unconstrained trial-and-error interactions in the real world can have undesirable consequences for the robot or its environment. To overcome such limitations, we propose a novel reinforcement learning architecture, OptLayer, that takes as inputs possibly unsafe actions predicted by a neural network and outputs the closest actions that satisfy chosen constraints. While learning control policies often requires carefully crafted rewards and penalties while exploring the range of possible actions, OptLayer ensures that only safe actions are actually executed and unsafe predictions are penalized during training. We demonstrate the effectiveness of our approach on robot reaching tasks, both simulated and in the real world.

OptLayer: Constrained Optimization For Safe Deep Reinforcement Learning

The paper presents OptLayer, a novel reinforcement learning framework integrating constrained optimization within deep reinforcement learning (DRL) to ensure safety in real-world applications. The central idea behind OptLayer is to amend action predictions generated by DRL with a layer that ensures compliance with safety constraints. This enables robots to operate safely in dynamic environments without violating physical or operational constraints, a critical issue when DRL is applied directly.

Motivation and Methodology

The challenge addressed by this paper is the difficulty of implementing DRL in real-world robotics due to potential risks posed by unrestricted exploration and learning processes. Specific problems such as robot collisions or damage to objects in its environment necessitate a more cautious approach to learning policies than current DRL methodologies allow.

OptLayer is introduced as a solution using a prediction-correction architecture where potentially unsafe actions generated by neural networks are corrected by solving a Quadratic Programming (QP) problem within a constrained optimization layer (OptLayer). This involves:

• Objective Function: The QP formulation minimizes the action's squared distance from neural network predictions subject to prescribed constraints.
• Constraints: They include joint limits, velocity bounds, torque restrictions, and environmental collision constraints. These constraints ensure actions are feasible within the robot's operational scope.
• Implementation: The QP solution is integrated into the DRL framework to allow gradient flow during training, maintaining the end-to-end learning capability of neural networks.

Experimental Results and Analysis

OptLayer's performance was verified through simulations and real-world tests using a 6-DoF UR5 robot tasked with reaching targets in the presence of obstacles. The experiments compared four distinct approaches for training policies — analyzing different aspects of utilizing the constraints during the training process.

1. Unconstrained Predictions (UP): Baseline method without applying OptLayer, resulting in suboptimal safety performance due to unrestricted exploration.
2. Constrained — Learn Predictions (CP): Actions are optimized through OptLayer only during execution, not during learning updates. The network learns nominal predictions while OptLayer ensures real-time safety.
3. Constrained — Learn Corrections (CC): Training the policy directly with actions adjusted by the constrained layer often results in inferior learning due to mismatch with neural predictions.
4. Constrained — Learn Predictions and Corrections (CPC): Combines elements of CP and CC, where the network is updated with both original predictions penalized for constraint violations and corrected actions. This approach showed improved convergence and learning efficiency.

CPC emerged as a superior strategy, combining safety during execution with effective learning updates, leading to better policy performance in both simulated and real-world scenarios. The methodology allowed the robots to safely navigate environments with static and moving obstacles, illustrating OptLayer's capacity to ensure constraint satisfaction without compromising the robot's task performance.

Implications and Future Prospects

This paper's contributions lie in demonstrating the feasibility of safely integrating reinforcement learning into physical systems where operational safety is paramount. It provides a notable example of how structured optimization can enhance the applicability of neural networks in DRL by imposing necessary operational limits.

Further development could involve exploring improved strategies for dynamic constraints or learning under model uncertainty. Additionally, advancements in perception systems and more nuanced understanding of uncertainty could expand the applicability across various complex domains such as finance and healthcare, strengthening decision-making robustness with more sophisticated and adaptive policy learning. Future work can also focus on exploring the scalability of OptLayer to more complex tasks like multi-agent coordination or manipulator dexterity.

Overall, OptLayer represents a significant step in bridging the gap between the theoretical prowess of DRL and its practical applicability in real-world scenarios laden with inherent operational risks.