Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization (1603.00448v3)

Abstract: Reinforcement learning can acquire complex behaviors from high-level specifications. However, defining a cost function that can be optimized effectively and encodes the correct task is challenging in practice. We explore how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high-dimensional robotic systems. Our method addresses two key challenges in inverse optimal control: first, the need for informative features and effective regularization to impose structure on the cost, and second, the difficulty of learning the cost function under unknown dynamics for high-dimensional continuous systems. To address the former challenge, we present an algorithm capable of learning arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering. To address the latter challenge, we formulate an efficient sample-based approximation for MaxEnt IOC. We evaluate our method on a series of simulated tasks and real-world robotic manipulation problems, demonstrating substantial improvement over prior methods both in terms of task complexity and sample efficiency.

• The paper advances RL by integrating policy optimization within IOC, enabling efficient learning of nonlinear cost functions from high-dimensional demonstrations.
• It employs deep neural networks to represent cost functions, reducing reliance on hand-crafted features and easing robotic task design.
• The method achieves impressive results in simulated and real-world tasks, demonstrating improved sample efficiency and robustness compared to previous approaches.

Overview of "Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization"

"Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization" by Chelsea Finn, Sergey Levine, and Pieter Abbeel advances the field of reinforcement learning (RL) by addressing some fundamental challenges in inverse optimal control (IOC). The authors introduce a method to learn complex behaviors from demonstrations, particularly focusing on applications in torque control for high-dimensional robotic systems. Their approach, termed "guided cost learning," integrates policy optimization within the IOC framework to facilitate learning of nonlinear cost functions without extensive feature engineering.

Key Contributions

1. Deep Nonlinear Cost Functions: The paper demonstrates how to use expressive, nonlinear function approximators, such as neural networks, to represent cost functions. This diminishes the reliance on manually designed features that have typically been indispensable in IOC, thus reducing the engineering complexity.
2. Efficient Sample-Based Approximation for MaxEnt IOC: To tackle the challenge of computing the IOC partition function in high-dimensional continuous systems with unknown dynamics, the authors formulate an efficient sample-based approximation under the maximum entropy (MaxEnt) IOC model. This is a critical improvement because traditional methods that solve the forward problem iteratively within an IOC optimization loop are computationally infeasible for complex robotic systems.
3. Adaptive Sampling via Policy Optimization: The guided cost learning algorithm adapts the sample distribution via policy optimization, which is paramount for regions of the trajectory space with lower cost. This process involves iterating between updating the cost function and refining the policy through sample-efficient reinforcement learning.

Methodology

The authors build upon the probabilistic MaxEnt IOC framework, which models demonstrated behaviors as stochastic and near-optimal relative to an unknown cost function. The novel aspect lies in their policy optimization approach, which employs time-varying linear models to adaptively sample trajectories. This optimization is grounded on fitting time-varying linear dynamics to samples from the current trajectory distribution, thereby refining the policy iteratively through a modified LQR backward pass.

Key equations in their approach include modeling the probability of a trajectory $\tau$ under a cost $c(\tau)$ with: $p(\tau) = \frac{1}{Z}\exp(-c(\tau)),$ and approximating the partition function $Z$ using importance sampling.

Experimental Evaluation

The paper provides comprehensive evaluations through both simulated and real-world robotic tasks:

1. Simulated Tasks: The authors conduct experiments on tasks including 2D navigation, 3D reaching, and peg insertion, showing significant improvement over previous methods in terms of task complexity and sample efficiency. The simulations illustrate the utility of their method in handling nonlinear cost functions and complex, high-dimensional trajectories.
2. Real-World Robotic Tasks: Two robotic manipulation tasks were explored: dish placement and pouring almonds. For the pouring task, the authors leveraged an unsupervised visual feature learning method to incorporate visual inputs, demonstrating the method's capability to use raw sensory data. This highlights the potential of guided cost learning in practical applications where manual cost design is particularly challenging.

Results

The paper reports strong numerical results, demonstrating both the efficacy and efficiency of their method. For instance, on the real-world dish placement task, the guided cost learning achieved a 100% success rate, while the comparative method, relative entropy IRL, had a 0% success rate. Such results affirm the robustness and applicability of guided cost learning in acquiring complex manipulation skills.

Implications and Future Directions

The implications of this work are substantial both theoretically and practically:

• Theoretical Advances: By integrating policy optimization within the IOC framework and employing deep learning for cost representation, this work paves the way for more flexible and powerful learning algorithms in RL. The elimination of manual feature engineering reduces the entry barrier for deploying RL in various complex domains.
• Practical Utility: This method can be particularly impactful in robotics, where designing accurate cost functions manually is cumbersome. Real-world robotic systems benefit from this approach through improved learning of manipulation skills directly from demonstrations.

Speculations on Future Developments

Future research could explore the application of guided cost learning to even more diverse domains, including autonomous driving and healthcare robotics. Enhancements can also be made in regularization techniques for cost functions represented by neural networks, particularly when dealing with high-dimensional visual inputs. Bridging the gap between learned policies and cost generalization across varying task conditions is another intriguing direction, potentially improving the transferability of learned behaviors.

In summary, "Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization" presents a significant step forward in the field of RL and robotics, offering powerful tools for learning complex behaviors from demonstrations with high efficacy and efficiency.