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Transductive Off-policy Proximal Policy Optimization

Yaozhong Gan, Renye Yan, Xiaoyang Tan, Zhe Wu, Junliang Xing

TL;DR

ToPPO addresses the inefficiency of on-policy PPO by leveraging off-policy data through a transductive, off-policy surrogate bound based on $A^{\mu}$ rather than $A^{\pi_k}$. It derives a policy-improvement lower bound and casts updates as a constrained optimization with PPO-like clipping, enabling monotonic improvement while avoiding the need to store many old-policy networks. The approach is validated on MuJoCo and Atari, where ToPPO shows improved sample efficiency and stability over PPO and competitive baselines, and is shown to mitigate biases associated with off-policy advantage estimation in prior methods. Overall, ToPPO provides both theoretical guarantees and practical guidance for effectively reusing off-policy data in policy optimization, with potential impact on sample efficiency in continuous control and related domains.

Abstract

Proximal Policy Optimization (PPO) is a popular model-free reinforcement learning algorithm, esteemed for its simplicity and efficacy. However, due to its inherent on-policy nature, its proficiency in harnessing data from disparate policies is constrained. This paper introduces a novel off-policy extension to the original PPO method, christened Transductive Off-policy PPO (ToPPO). Herein, we provide theoretical justification for incorporating off-policy data in PPO training and prudent guidelines for its safe application. Our contribution includes a novel formulation of the policy improvement lower bound for prospective policies derived from off-policy data, accompanied by a computationally efficient mechanism to optimize this bound, underpinned by assurances of monotonic improvement. Comprehensive experimental results across six representative tasks underscore ToPPO's promising performance.

Transductive Off-policy Proximal Policy Optimization

TL;DR

ToPPO addresses the inefficiency of on-policy PPO by leveraging off-policy data through a transductive, off-policy surrogate bound based on rather than . It derives a policy-improvement lower bound and casts updates as a constrained optimization with PPO-like clipping, enabling monotonic improvement while avoiding the need to store many old-policy networks. The approach is validated on MuJoCo and Atari, where ToPPO shows improved sample efficiency and stability over PPO and competitive baselines, and is shown to mitigate biases associated with off-policy advantage estimation in prior methods. Overall, ToPPO provides both theoretical guarantees and practical guidance for effectively reusing off-policy data in policy optimization, with potential impact on sample efficiency in continuous control and related domains.

Abstract

Proximal Policy Optimization (PPO) is a popular model-free reinforcement learning algorithm, esteemed for its simplicity and efficacy. However, due to its inherent on-policy nature, its proficiency in harnessing data from disparate policies is constrained. This paper introduces a novel off-policy extension to the original PPO method, christened Transductive Off-policy PPO (ToPPO). Herein, we provide theoretical justification for incorporating off-policy data in PPO training and prudent guidelines for its safe application. Our contribution includes a novel formulation of the policy improvement lower bound for prospective policies derived from off-policy data, accompanied by a computationally efficient mechanism to optimize this bound, underpinned by assurances of monotonic improvement. Comprehensive experimental results across six representative tasks underscore ToPPO's promising performance.
Paper Structure (21 sections, 5 theorems, 40 equations, 5 figures, 1 table)

This paper contains 21 sections, 5 theorems, 40 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Policy Improvement Lower Bound
  4. Generalized Proximal Policy Optimization
  5. Transductive Off-policy PPO
  6. Derivation of the Constrained Optimization Problem
  7. The Clipped Surrogate Objection
  8. Selecting policies
  9. Discussion
  10. Reanalyze the PPO Algorithm
  11. Reanalyze the GePPO Algorithm
  12. Experiments
  13. Evaluation
  14. Performance improvement
  15. Ablation Studies
  16. ...and 6 more sections

Key Result

Lemma 2.1

(Policy Improvement Lower Bound) Consider a current policy $\pi_{k}$, and any policies $\pi$ and $\mu$, we have where $\epsilon=\max _{s, a}\left| A^{\pi_k}(s, a)\right|$, $\delta^{\pi, \mu}=\mathbb{E}_{s \sim \rho^{\mu}} \mathbb{D}_{\mathcal{T} \mathcal{V}}(\mu, \pi)(s)$, and $\delta_{\max}^{\pi_k, \pi} = \max_s \mathbb{D}_{\mathcal{T} \mathcal{V}}(\pi_k, \pi)(s)$. $\mathbb{D}_{\mathcal{T} \math

Figures (5)

  • Figure 1: The ratio of the difference between the true $V^{\pi}$ values and the estimated $\hat{V}$ values using V-trace technique to the true $V^{\pi}$ values, i.e.$\hbox{V ration}=|\frac{V^{\pi}-\hat{V}}{V^{\pi}}|$.
  • Figure 2: Learning curves on the MuJoCo environments. Performance of ToPPO vs. PPO, OTRPO, TRPO, DISC, OPPO, and GePPO. The shaded region indicates the standard deviation of ten random seeds. The X-axis represents the timesteps in the environment.
  • Figure 3: Final performance of ToPPO vs. ToPPO NOT (remove the constraints of selecting policies)
  • Figure 4: Learning curves on the Atari environments. Performance of ToPPO vs. PPO. The shaded region indicates the standard deviation of three random seeds.
  • Figure 5: Learning curves on the some Atari environments. Performance of ToPPO vs. PPO. The shaded region indicates the standard deviation of three random seeds.

Theorems & Definitions (12)

  • Lemma 2.1
  • Lemma 3.1
  • Lemma A.1
  • proof
  • Lemma A.2
  • Lemma A.3
  • proof
  • proof
  • proof
  • proof
  • ...and 2 more