Learning Parameterized Skills

Abstract: We introduce a method for constructing skills capable of solving tasks drawn from a distribution of parameterized reinforcement learning problems. The method draws example tasks from a distribution of interest and uses the corresponding learned policies to estimate the topology of the lower-dimensional piecewise-smooth manifold on which the skill policies lie. This manifold models how policy parameters change as task parameters vary. The method identifies the number of charts that compose the manifold and then applies non-linear regression in each chart to construct a parameterized skill by predicting policy parameters from task parameters. We evaluate our method on an underactuated simulated robotic arm tasked with learning to accurately throw darts at a parameterized target location.

• The paper introduces a framework that models policy representations as a piecewise-smooth manifold to construct transferable skills in reinforcement learning.
• It validates the approach using a robotic arm dart-throwing task, achieving a policy parameter error reduction to 3% with only 15 samples.
• The method cuts required policy updates from 22 to 2, significantly enhancing task adaptability and efficiency in high-dimensional control problems.

Overview of "Learning Parameterized Skills"

The paper "Learning Parameterized Skills" by Bruno Castro da Silva, George Konidaris, and Andrew G. Barto contributes to the domain of Reinforcement Learning (RL) by introducing a framework for the construction of parameterized skills. This approach is designed to address the challenge of applying reinforcement learning to high-dimensional control problems where tasks are drawn from a distribution of parameterized tasks. By modelling the parameter space as a lower-dimensional manifold, this work extends the notion of skill acquisition in RL, facilitating the synthesis of skills that can be applied across different yet related tasks.

Methodology

The authors propose a novel approach that leverages the manifold structure of policy representations. This is achieved through a process that involves:

1. Sampling task instances from a distribution of interest.
2. Using learned policies from these tasks to estimate the topology of a piecewise-smooth manifold, upon which policy parameters change with task parameters.
3. Identifying the number of disjoint sub-manifolds (or charts) that constitute this manifold.
4. Applying non-linear regression within these charts to form a parameterized skill. This skill allows for the prediction of policy parameters based on given task parameters.

The method is validated using a complex example involving an underactuated robotic arm tasked with learning to throw darts accurately at various target locations.

Evaluation and Results

In the evaluation domain, the robotic arm's dart-throwing task, the authors utilize Dynamic Movement Primitives (DMPs) to represent policies. Through the PoWER algorithm, the policy representation is optimized to achieve near-target precision in dart throws. ISOMAP is employed to analyze the geometry of the policy space and effectively identify disconnected policy manifolds. This investigation reveals that the policy space is occupied by lower-dimensional surfaces, correlating policy configurations with task variations.

Numerical Results:

• The average policy parameter error is demonstrated to decrease to approximately 3% with 15 samples, highlighting improved accuracy in skill prediction.
• Initial solutions achieved a 70% initial accuracy with predicted policies landing within 70cm of target centers without further learning.
• The requirement for policy updates is significantly reduced from 22 to 2 when using parameterized skills trained with sufficient samples.

Implications

The proposed framework demonstrates practical advancements in skill transfer and generalization within RL. By parameterizing skills over a manifold, the approach allows for efficient application of RL in environments with task variability. This has direct implications for robotics and other fields where dynamic task adaptation is crucial.

Future Directions

The framework presents avenues for further exploration in enhancing RL capabilities:

• Adaptive Task Sampling: Developing strategies to select training tasks actively, maximizing skill effectiveness over expected task distributions.
• Handling Non-Stationary Tasks: Addressing changes in task distributions, potentially using adaptive resampling mechanisms.
• Efficient Manifold Analysis: Improving methods to ascertain manifold topology with reduced task sampling.

Comparison with Related Work

Previous efforts in RL addressed similar challenges either through single MDP formulations incorporating task parameters or through skill transfer mechanisms, often without explicitly addressing the manifold nature of policy representation. This paper distinguishes itself by using a geometric perspective, providing a robust and flexible approach to skill parameterization.

In conclusion, "Learning Parameterized Skills" provides a comprehensive methodology for deriving adaptable skills from a limited set of task permutations. It effectively bridges policy learning with task flexibility, positioning itself as a significant contribution to hierarchical RL and skill abstraction. This work paves the way for more generalized and efficient learning paradigms in complex, variable task environments.