Efficient Exploration via State Marginal Matching (1906.05274v3)

Abstract: Exploration is critical to a reinforcement learning agent's performance in its given environment. Prior exploration methods are often based on using heuristic auxiliary predictions to guide policy behavior, lacking a mathematically-grounded objective with clear properties. In contrast, we recast exploration as a problem of State Marginal Matching (SMM), where we aim to learn a policy for which the state marginal distribution matches a given target state distribution. The target distribution is a uniform distribution in most cases, but can incorporate prior knowledge if available. In effect, SMM amortizes the cost of learning to explore in a given environment. The SMM objective can be viewed as a two-player, zero-sum game between a state density model and a parametric policy, an idea that we use to build an algorithm for optimizing the SMM objective. Using this formalism, we further demonstrate that prior work approximately maximizes the SMM objective, offering an explanation for the success of these methods. On both simulated and real-world tasks, we demonstrate that agents that directly optimize the SMM objective explore faster and adapt more quickly to new tasks as compared to prior exploration methods.

• The paper introduces a novel objective that reformulates exploration as matching the policy's state distribution to a target distribution.
• The paper proposes an algorithm based on fictitious play that alternates updates between the policy and a state density model to ensure convergence in exploration tasks.
• The paper demonstrates empirical success across simulated and robotic domains, highlighting enhanced exploration efficiency and adaptability.

Overview of "Efficient Exploration via State Marginal Matching"

The paper introduces a novel framework for exploration in reinforcement learning (RL), termed State Marginal Matching (SMM). While many existing exploration methods rely on heuristic strategies without a grounded mathematical foundation, SMM offers a formal objective: matching the state marginal distribution of a policy to a specified target distribution. This approach redefines exploration as a distribution matching problem, aiming for efficiency and adaptability across multiple tasks.

State Marginal Matching Framework

The SMM framework proposes that exploration efficiency can be optimized through an objective that aligns the state marginal distribution $\rho_{\pi}(s)$ induced by a policy $\pi$ with a target distribution $p^*(s)$. Typically, $p^*(s)$ is a uniform distribution, encouraging the policy to visit all possible states equitably. However, it can also be tailored to incorporate prior domain knowledge or specific task requirements.

At its core, the SMM objective can be viewed as a two-player, zero-sum game between the policy and a state density model. This game-theoretic perspective provides insights into the behavior and performance of exploration strategies, showing that prior techniques approximate the SMM goal implicitly.

Algorithmic Contribution

The authors propose an algorithm based on fictitious play, a classical game-theoretic method known to converge in zero-sum games. This technique alternates between updating the policy and the density model, effectively learning a mixture of policies that collectively achieve state marginal matching over training iterations. This is a significant deviation from traditional greedy optimization procedures, which might suffer from non-convergence or oscillatory dynamics.

Empirical Results

The paper provides strong empirical results demonstrating the efficiency of SMM across various domains, including both simulated and real-world robotic tasks. Agents using the SMM objective are shown to explore more broadly and adapt more swiftly compared to traditional methods. The addition of mixture modeling (SM4) enhances exploration by allowing the decomposing of complex target distributions into simpler, component-aligned policies.

Implications and Future Work

The recasting of exploration as an SMM problem has several implications. Firstly, it provides a quantifiable metric for good exploration, enabling better evaluation and comparison of exploration algorithms. Additionally, the framework's adaptability suggests potential applications in meta-learning scenarios, where rapid adaptation to new tasks with minimal data is crucial.

As a forward-looking statement, the integration of SMM with more advanced model architectures and the exploration of dynamic target distributions could further enhance RL performance in highly variable environments.

In conclusion, the State Marginal Matching framework offers a rigorous, principled approach to exploration in reinforcement learning, potentially paving the way for more robust, efficient, and adaptable RL systems.