insight - Reinforcement learning machine learning - # Computational complexity of reinforcement learning versus supervised learning

Core Concepts

Exploration in reinforcement learning is computationally harder than prediction in supervised learning, under a plausible cryptographic hardness assumption.

Abstract

The paper investigates the computational complexity of reinforcement learning (RL) compared to supervised learning (regression). It focuses on a specific class of Markov decision processes (MDPs) called block MDPs, where the observed states are stochastic emissions from a smaller latent state space.
The key insights are:
The paper constructs a family of block MDPs where reward-free RL (exploring the entire state space) is computationally harder than realizable regression (predicting labels given covariates), under a cryptographic hardness assumption.
The paper also shows that even in reward-directed RL, the natural regression oracle is not sufficient for computationally efficient learning, and some stronger oracle is necessary.
The technical proofs involve novel reductions between RL in block MDPs and variants of the Learning Parities with Noise (LPN) problem, a well-studied cryptographic hardness assumption. This includes showing the robustness of LPN to weakly dependent noise.
The results suggest that exploration, a core challenge in RL, is fundamentally harder than prediction, which is the focus of supervised learning. This provides a complexity-theoretic separation between these two modes of learning.
The paper also discusses special cases of block MDPs where RL is tractable given access to a regression oracle, highlighting the importance of understanding the structural assumptions that make RL computationally easier.

Stats

None.

Quotes

None.

Key Insights Distilled From

by Noah Golowic... at **arxiv.org** 04-08-2024

Deeper Inquiries

The computational separation between exploration and prediction in reinforcement learning (RL) has significant implications for the design of practical RL algorithms.
Algorithm Design: The separation highlights the inherent complexity of exploration in RL compared to prediction tasks. This suggests that algorithms focusing on exploration strategies need to be carefully designed to handle the additional computational challenges. It may lead to the development of more sophisticated exploration techniques that can efficiently navigate complex environments.
Oracle Selection: The results emphasize the importance of selecting appropriate oracles for RL algorithms. The choice of oracle can significantly impact the algorithm's performance and efficiency. Understanding the minimal oracle required for RL tasks can guide the design of more effective algorithms.
Specialized Approaches: The separation underscores the need for specialized approaches for different aspects of RL. Algorithms tailored for prediction tasks may not be sufficient for exploration, and vice versa. This could lead to the development of hybrid algorithms that combine strategies for both exploration and prediction to achieve optimal performance.
Complexity Analysis: The computational separation provides insights into the underlying complexity of RL tasks. By understanding the specific computational challenges associated with exploration, researchers can focus on addressing these challenges to improve the efficiency and effectiveness of RL algorithms.

The technical techniques developed in this paper, such as the use of cryptographic assumptions and reductions between learning problems, can potentially be applied to prove hardness results for other RL settings beyond block MDPs.
Generalization: The methodologies and frameworks established in this work can be adapted and extended to analyze the computational complexity of RL in different environments or under varying assumptions. By modifying the constructions and proofs, similar hardness results could be derived for diverse RL scenarios.
Algorithmic Analysis: The techniques used to separate exploration and prediction in block MDPs can be leveraged to investigate the computational challenges in other RL settings. By applying similar reduction techniques and cryptographic assumptions, researchers can explore the hardness of learning tasks in various RL frameworks.
Comparative Studies: The methods employed in this paper can facilitate comparative studies of computational complexity in different RL contexts. By establishing computational separations and oracle lower bounds, researchers can gain a deeper understanding of the algorithmic requirements and limitations in various RL scenarios.

While the open-loop indistinguishability property highlighted in this work presents a significant computational challenge for RL, there may be other natural structural assumptions on the environment dynamics that could make RL computationally easier than the regression problem.
Sparse Rewards: Environments with sparse reward structures, where the reward signal is informative and easily discernible, may simplify the RL problem. Algorithms can focus on maximizing the sparse rewards without the need for extensive exploration, potentially reducing the computational complexity compared to regression tasks.
Low-Dimensional State Space: Environments with a low-dimensional state space that directly correlates with the optimal policy could make RL easier. If the state space provides clear indicators for action selection, the RL problem may be more tractable, requiring less computational effort than regression tasks.
Structured Dynamics: Environments with structured dynamics that align with the agent's objectives could facilitate efficient RL. If the dynamics of the environment are well-aligned with the agent's goals, the exploration-exploitation trade-off may be more straightforward, leading to computational advantages over regression problems.
By identifying and leveraging such natural structural assumptions in the environment dynamics, researchers may uncover scenarios where RL is computationally easier than traditional regression tasks, offering new insights into the complexity of RL problems.

0