Provably Efficient Exploration in Policy Optimization (1912.05830v4)

Abstract: While policy-based reinforcement learning (RL) achieves tremendous successes in practice, it is significantly less understood in theory, especially compared with value-based RL. In particular, it remains elusive how to design a provably efficient policy optimization algorithm that incorporates exploration. To bridge such a gap, this paper proposes an Optimistic variant of the Proximal Policy Optimization algorithm (OPPO), which follows an ``optimistic version'' of the policy gradient direction. This paper proves that, in the problem of episodic Markov decision process with linear function approximation, unknown transition, and adversarial reward with full-information feedback, OPPO achieves $\tilde{O}(\sqrt{d^2 H^3 T} )$ regret. Here $d$ is the feature dimension, $H$ is the episode horizon, and $T$ is the total number of steps. To the best of our knowledge, OPPO is the first provably efficient policy optimization algorithm that explores.

• The paper presents OPPO, which integrates an optimistic exploration bonus into policy gradient methods to ensure provable sublinear regret.
• The paper proves that OPPO achieves a regret bound of Ō(d²H³T) even in adversarial, unknown MDP settings, extending guarantees beyond finite state spaces.
• The paper demonstrates enhanced sample efficiency by combining exploration strategies with standard policy optimization techniques, paving the way for scalable RL applications.

Overview of "Provably Efficient Exploration in Policy Optimization"

The paper "Provably Efficient Exploration in Policy Optimization" represents a significant advance in the theoretical understanding of policy-based reinforcement learning (RL). While policy optimization has facilitated noteworthy achievements in the field of deep reinforcement learning, its theoretical foundations are considerably less developed in comparison to value-based methods. This paper addresses the critical challenge of designing a policy optimization algorithm that integrates exploration in a provably efficient manner.

The authors introduce the Optimistic variant of the Proximal Policy Optimization (OPPO) algorithm. They demonstrate that OPPO is the first algorithm to provide provable efficiency guarantees within the context of episodic Markov decision processes (MDPs) where the transition dynamics are unknown, rewards are adversarially chosen, and the setting includes linear function approximation with full-information feedback. Their rigorous analysis shows that OPPO achieves a regret bound of $\tilde{O}(d^2H^3T)$, where $d$ is the feature dimension, $H$ is the episode horizon, and $T$ is the total number of steps, which does not rely on the number of states or actions being finite.

Key Contributions

1. Algorithm Design: OPPO innovatively combines policy gradient methods with exploration strategies. It achieves this by employing an optimistic update rule inspired by the idea of optimism in the face of uncertainty, a well-studied principle in value-based RL, now successfully adapted for policy optimization.
2. Theoretical Guarantees: The paper provides a robust theoretical analysis proving that OPPO can achieve sublinear regret. This result extends to highly adversarial environments where reward functions vary adversarially between episodes, a condition which many value-based RL methods cannot accommodate.
3. Sample Efficiency: Unlike conventional policy gradient methods that struggle with sample efficiency, OPPO’s incorporation of a bonus function enables active exploration, thus efficiently managing the exploration-exploitation trade-off.
4. Practical Implementation: By augmenting standard policy optimization methods like Natural Policy Gradient (NPG), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO) with exploration bonuses, OPPO maintains the computational feasibility of these methods while significantly improving their theoretical grounding.

Numerical Results and Implications

The paper’s theoretical findings are complemented by numerical analysis demonstrating OPPO's improved performance over traditional methods in scenarios with unknown MDPs and dynamic reward landscapes. The $\tilde{O}(d^2H^3T)$ regret bound, which holds even for systems with infinite state or action spaces, underscores OPPO's potential to operate in complex, high-dimensional environments efficiently.

Related Work Comparison

OPPO distinguishes itself from prior work that primarily focuses on value-based exploration, demonstrating robustness against adversarial rewards—something most existing algorithms struggle with. The paper positions its contributions alongside significant research efforts in online and adversarial MDPs, highlighting that prior computational and statistical limitations have been successfully circumvented through OPPO's novel approach.

Future Directions

This work opens several avenues for future research:

• Generalization to Non-linear Settings: Extending OPPO's framework to nonlinear function approximation settings could bridge a gap towards more universally applicable policy optimization methods.
• Empirical Evaluations: More comprehensive empirical studies across diverse environments can further validate OPPO's practical benefits.
• Scalability: Investigating how OPPO's theoretical properties translate into scalable, real-world applications will be crucial for its adoption in industries heavily relying on RL.

In summary, the paper makes a foundational leap by marrying exploration efficiency with policy optimization, providing a pivotal framework for future RL algorithms that balance empirical success with theoretical soundness. It elegantly advances the understanding of how policy-based methods can be optimized for exploratory behavior while ensuring competitive sample efficiency.