Skew-Fit: State-Covering Self-Supervised Reinforcement Learning (1903.03698v4)

Abstract: Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills. Defining each skill with a manually-designed reward function limits this repertoire and imposes a manual engineering burden. Self-supervised agents that set their own goals can automate this process, but designing appropriate goal setting objectives can be difficult, and often involves heuristic design decisions. In this paper, we propose a formal exploration objective for goal-reaching policies that maximizes state coverage. We show that this objective is equivalent to maximizing goal reaching performance together with the entropy of the goal distribution, where goals correspond to full state observations. To instantiate this principle, we present an algorithm called Skew-Fit for learning a maximum-entropy goal distributions. We prove that, under regularity conditions, Skew-Fit converges to a uniform distribution over the set of valid states, even when we do not know this set beforehand. Our experiments show that combining Skew-Fit for learning goal distributions with existing goal-reaching methods outperforms a variety of prior methods on open-sourced visual goal-reaching tasks. Moreover, we demonstrate that Skew-Fit enables a real-world robot to learn to open a door, entirely from scratch, from pixels, and without any manually-designed reward function.

An Expert Analysis of "Skew-Fit: State-Covering Self-Supervised Reinforcement Learning"

The presented paper introduces Skew-Fit, a novel methodology within the field of self-supervised reinforcement learning (RL) aimed at addressing the challenges inherent in achieving comprehensive state coverage. Notably, it sets precedence by formalizing the goal-directed exploration objective to maximize the entropy of goal distributions within RL contexts. The significance of this work lies not only in its novel theoretical contributions but also in its practical ability to augment exploration efficiency in complex domains where specifying reward functions is impractical or infeasible.

The core motivation for this research stems from the pursuit of enabling RL agents to autonomously develop a broad set of skills without extensive task-specific human intervention. Where traditional RL necessitates manually engineered reward functions to define skills, Skew-Fit introduces a self-supervised alternative by enabling agents to autonomously define and pursue goals.

Theoretical Contributions

Skew-Fit's framework is rooted in the principle of maximizing state entropy. The paper articulates a dual-objective structure to optimize for state coverage: concurrently maximizing the goal-reaching performance and the entropy of the goal distribution. This equivalence is crucial as it lays a structured foundation for the exploration problem where the agent crafts its own objectives. The authors introduce a maximum-entropy goal-setting algorithm, Skew-Fit, and provide proof of its convergence to a uniform distribution over valid states under specific regularity conditions, even when the complete state space is unknown.

A noteworthy aspect is the method's reliance on sampling importance resampling (SIR) to handle variance issues that would typically be associated with importance sampling techniques. This integration of SIR achieves both practical utility and theoretical robustness in evaluating sampling distributions, enhancing Skew-Fit's impact within broader RL exploration strategies.

Empirical Analysis

The empirical validation showcases Skew-Fit's superior performance across a suite of simulated RL tasks involving goal-conditioned exploration in high-dimensional visual environments. The paper presents a rigorous comparison against state-of-the-art methods like Hindsight Experience Replay (HER) and various alternative strategies for goal sampling and setting.

Experimental results reveal that Skew-Fit enables superior state exploration coverage, leading to higher entropy of state visitation distributions. Significantly, Skew-Fit outperforms prior methods in an ant navigation task and multiple vision-based robot manipulation tasks. Furthermore, the practical effectiveness of Skew-Fit is demonstrated in a real-world door opening task, where a robot learns the skill entirely from pixels without any task-specific reward, indicating broad applicability beyond simulated environments.

Implications and Future Work

This research advances the theoretical understanding of self-supervised RL and highlights the practical potential of autonomous exploration strategies. The implications are substantial, offering promise for deploying RL agents in real-world tasks where manual reward design is onerous, such as autonomous robotics and adaptive planning systems.

Future work may expand upon the foundational insights provided by Skew-Fit by exploring applications in more dynamic and unstructured real-world environments. Further examination of the algorithm's robustness to various forms of environment stochasticity and scaling to cooperative multi-agent frameworks could open new avenues for enhancing exploration efficiency in a collaborative context. The paper also implicates further research into the trade-offs between exploration and exploitation in environments with complex state dynamics and transitioning mechanisms.

Conclusion

In summary, "Skew-Fit: State-Covering Self-Supervised Reinforcement Learning" offers a meaningful contribution to the field of reinforcement learning by providing an algorithm capable of achieving comprehensive state exploration through self-set goals. It harmonizes theoretical rigor with practical efficacy and invites subsequent research to build upon this framework, paving the way for more autonomous and adaptable RL systems in increasingly complex environments.