hierarchical proximal policy optimization

This work addresses the longstanding challenge in reinforcement learning of reconciling theoretical stability with engineering efficiency in policy optimization. Methodologically, we propose an unconstrained first-order algorithm that modifies the PPO policy loss by introducing a tighter probability-ratio clipping mechanism; this implicitly enforces a KL-divergence constraint—without requiring second-order computations or explicit constraints (e.g., trust-region projections)—thereby guaranteeing monotonic improvement and convergence. Our key contribution is the first unified framework achieving TRPO-level theoretical robustness while retaining PPO-level implementation simplicity and computational efficiency. Empirical evaluation across multiple benchmark tasks demonstrates consistent and significant performance gains over standard PPO, particularly excelling in end-to-end training of large-scale neural networks: the method achieves higher sample efficiency, improved training stability, and superior generalization performance.

When applying Generalized Reinforcement Policy Optimization (GRPO) to multi-turn interactive LLM agents for long-horizon reasoning tasks, instability in advantage estimation and policy degradation arise due to token-level optimization misaligned with the hierarchical structure of dialogue. Method: We propose a turn-level Markov Decision Process (MDP) modeling framework, elevating policy optimization granularity from tokens to dialogue turns. Building upon this, we design an enhanced GRPO algorithm integrating turn-level reward attribution, long-term credit assignment, and stabilized advantage estimation. Results: On WebShop and Sokoban benchmarks, our method significantly outperforms standard GRPO—improving success rates on long-reasoning tasks by over 18%, reducing training variance by 32%, and yielding more robust policy convergence. Our core contribution is the first reinforcement learning formulation that enables turn-level modeling and optimization for multi-turn interactive agents, establishing a new paradigm for stable and efficient LLM agent training.

Proximal Policy Optimization (PPO) suffers from high variance and high sample complexity, undermining training stability and efficiency. To address this, we propose HP3O—a novel PPO variant that introduces *trajectory recency modeling* into the PPO framework for the first time. Specifically, HP3O employs a FIFO trajectory replay buffer to reuse recent high-return trajectories and designs a mixed policy update mechanism under distributional shift constraints, jointly leveraging optimal and randomly sampled trajectories. We theoretically establish its monotonic policy improvement guarantee. Empirical evaluation on multiple continuous-control benchmark tasks demonstrates that HP3O significantly improves sample efficiency and training stability over standard PPO, A2C, and PPO-RND: it reduces policy gradient variance by 32% and consistently achieves superior final performance.

This work addresses the long-standing lack of theoretical unification between the two predominant variants of Proximal Policy Optimization (PPO)—namely, PPO with a clipped surrogate objective (PPO-Clip) and PPO with KL divergence penalty. By analyzing the per-sample KL divergence, the study reveals that PPO-Clip implicitly implements a sample-wise KL regularization with a stepwise coefficient. Through closed-form derivations involving the importance sampling ratio and the advantage function, the authors construct an adaptive KL penalty coefficient that exactly reproduces the gradient updates of PPO-Clip. Empirical validation on five MuJoCo continuous control tasks demonstrates nearly identical training curves between this reformulation and the original PPO-Clip, confirming their theoretical equivalence. This insight not only provides a unified interpretation of PPO but also opens new avenues for algorithm design based on adaptive KL regularization.

To address the low sample efficiency and weak exploration capability of online reinforcement learning in resource-constrained settings, this paper proposes DiffPPO—the first integration of denoising diffusion probabilistic models (DDPMs) into the PPO framework. DiffPPO synthesizes high-fidelity synthetic trajectories to augment offline datasets, enabling an offline-online hybrid training paradigm. Methodologically, it jointly leverages trajectory generation and importance reweighting to facilitate effective policy transfer from online learning to computationally limited offline environments. Experiments on challenging continuous control benchmarks demonstrate that DiffPPO significantly improves cumulative reward (+23.6%), accelerates convergence (reducing training steps by 37%), and enhances policy stability. The complete implementation is open-sourced, establishing a reproducible, diffusion-augmented PPO paradigm for sample-efficient RL.

This work addresses the disconnect between trust region theory and the heuristic clipping objective in Proximal Policy Optimization (PPO) by introducing the Bounded Regularized Reinforcement Learning (BRRL) framework. The authors formulate a constrained regularized policy optimization problem, derive its closed-form optimal solution, and design the Bounded Policy Optimization (BPO) algorithm to minimize the advantage-weighted divergence between the parameterized policy and this solution. They further extend BPO to Generalized BPO (GBPO) for large language model (LLM) fine-tuning. This study provides the first theoretical justification for PPO-style methods, unifying trust region optimization with a cross-entropy perspective while guaranteeing monotonic policy improvement. Experiments demonstrate that BPO and GBPO consistently match or outperform PPO and GRPO across MuJoCo, Atari, IsaacLab, and LLM fine-tuning benchmarks in both stability and final performance.

This work addresses the trade-off between training stability and sample efficiency in deep reinforcement learning, where Proximal Policy Optimization (PPO) exhibits stable on-policy learning but suffers from low sample efficiency, while off-policy methods, though more efficient, are prone to estimation bias and variance. To reconcile these limitations, the paper proposes ExO-PPO, which constructs an expectation-based lower bound for generalized policy improvement, introduces a piecewise exponential clipping mechanism, and incorporates a multi-policy replay buffer to reuse historical trajectories. This approach preserves the stability of on-policy optimization while substantially enhancing sample efficiency. Experimental results demonstrate that ExO-PPO consistently outperforms standard PPO and its state-of-the-art variants across a range of tasks, achieving significant improvements in stability, sample efficiency, and overall performance.

This work addresses the bias in advantage estimation arising from inconsistent historical contexts in stepwise grouped policy optimization. To mitigate this issue, the authors propose a hierarchical grouping mechanism that clusters each step within a trajectory into multiple levels based on contextual consistency, computes advantage values independently within each group, and aggregates them via adaptive weighting to yield more accurate stepwise advantage estimates. The method incurs no additional model or sampling overhead and effectively reduces bias induced by context inconsistency, substantially improving policy optimization performance in long-horizon tasks. Evaluated on the ALFWorld and WebShop benchmarks, agents built upon the Qwen2.5 series of large language models significantly outperform existing reinforcement learning approaches, achieving superior results under identical computational constraints.

Existing hierarchical decision-making approaches often struggle to simultaneously satisfy constraints and maintain computational efficiency due to misalignment between low-level policies and high-level objectives. This work proposes a principled inverse optimization–based hierarchical framework that, for the first time, systematically constructs structured low-level optimization problems from expert demonstrations, thereby aligning high-level task abstractions with low-level decision-making. By integrating inverse optimization, hierarchical reinforcement learning, and optimal control, the method achieves both interpretability and computational efficiency. Empirical evaluations on resource allocation and obstacle avoidance tasks demonstrate that the approach significantly outperforms end-to-end reinforcement learning, learning-augmented optimal control, and existing hierarchical methods, achieving state-of-the-art performance in both decision quality and computational speed.