Dynamics-Aware Unsupervised Discovery of Skills (1907.01657v2)

Abstract: Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms to generate a large variety of behaviors and solve diverse tasks. However, learning an accurate model for complex dynamical systems is difficult, and even then, the model might not generalize well outside the distribution of states on which it was trained. In this work, we combine model-based learning with model-free learning of primitives that make model-based planning easy. To that end, we aim to answer the question: how can we discover skills whose outcomes are easy to predict? We propose an unsupervised learning algorithm, Dynamics-Aware Discovery of Skills (DADS), which simultaneously discovers predictable behaviors and learns their dynamics. Our method can leverage continuous skill spaces, theoretically, allowing us to learn infinitely many behaviors even for high-dimensional state-spaces. We demonstrate that zero-shot planning in the learned latent space significantly outperforms standard MBRL and model-free goal-conditioned RL, can handle sparse-reward tasks, and substantially improves over prior hierarchical RL methods for unsupervised skill discovery.

• The paper presents a novel unsupervised framework that leverages mutual information to discover low-level skills for improved high-level planning.
• The paper utilizes a skill-conditioned transition model to predict state transitions, enhancing sample efficiency and enabling zero-shot generalization.
• The paper demonstrates that the DADS method outperforms conventional model-based and goal-conditioned approaches in sparse reward and complex environments.

Dynamics-Aware Unsupervised Discovery of Skills

The paper "Dynamics-Aware Unsupervised Discovery of Skills" presents a novel reinforcement learning approach designed to address the challenges of discovering skills for model-based planning in complex environments. The method, termed Dynamics-Aware Discovery of Skills (DADS), employs an unsupervised learning framework that integrates model-based and model-free reinforcement learning techniques to discover and represent diverse behaviors efficiently.

Core Contributions

DADS is formulated to discover low-level skills that make high-level planning more tractable. The methodology leverages a mutual-information-based exploration objective, a concept typically utilized in intrinsically motivated reinforcement learning, to optimize the predictability of state transitions conditional on learned skills. By focusing on the mutual information between the observed future state and the current skill, the approach encourages the development of skills that are both diverse and predictable, providing a robust foundation for subsequent hierarchical control.

Technical Highlights

The paper introduces an innovative approach to the problem of skill discovery in reinforcement learning frameworks, particularly when dealing with high-dimensional and complex dynamical systems. The goal is to develop skills that facilitate easier and more effective model-based planning. Here, the authors leverage mutual information as a fundamental objective to encourage exploration and the generation of a rich set of distinct skills:

• Mutual Information Framework: DADS maximizes the mutual information between the skills and the resultant state transitions to ensure that the discovered skills induce predictable outcomes. This approach allows for a dynamic embedding in continuous spaces, which stands in contrast to some conventional discrete skill-based methods.
• Skill-Dynamics: A noteworthy feature of this work is the incorporation of a learned skill-conditioned transition model, which serves as a basis for planning. This dynamic skill model enables the planner to predict and simulate the outcomes of executing a skill without interacting with the environment, thereby improving sample efficiency significantly.
• Unsupervised Skill Acquisition: Crucially, skills are learned without any explicit external rewards. Instead, through autonomous exploration, the DADS framework optimizes skills directly for predictability, resulting in a set of primitives that can later be composed using various forms of hierarchical control.

Experimental Results

Empirically, the paper provides evidence of the efficacy of DADS by showcasing zero-shot generalization capability in sparse reward environments. Notably, the system demonstrates superior performance in complex tasks when compared to standard model-based reinforcement learning (MBRL) methods and model-free goal-conditioned RL techniques. DADS effectively handles sparse-reward tasks and can substantially outperform comparable prior hierarchical reinforcement learning methods, such as DIAYN, by providing lower-variance skills which are crucial for effective task-solving in downstream applications.

Implications and Future Directions

This research underscores the promise of dynamics-aware learning for skill discovery, which stands to impact both theoretical developments and practical applications in AI. By bridging the gap between unsupervised skill learning and zero-shot task execution, DADS sets a new benchmark in the development of autonomous systems capable of efficiently handling a diverse set of high-dimensional and dynamic tasks.

Future research can explore extending non-parametric modeling techniques to further refine the approximation of the skill-dynamics, and potentially incorporate off-policy updates to optimize sample efficiency. The application of DADS in domains such as robotic manipulation or dynamic decision-making under uncertainty presents a fertile ground for further inquiry and experimentation.

By presenting a solid foundation for dynamics-aware skill learning, the authors contribute a powerful toolset for researchers and practitioners striving to improve the adaptive capabilities of autonomous agents in complex environments.