Beyond the Boundaries of Proximal Policy Optimization

• URL: http://arxiv.org/abs/2411.00666v1
• Date: Fri, 01 Nov 2024 15:29:10 GMT
• Title: Beyond the Boundaries of Proximal Policy Optimization
• Authors: Charlie B. Tan, Edan Toledo, Benjamin Ellis, Jakob N. Foerster, Ferenc Huszár,
• Abstract summary: This work offers an alternative perspective of PPO, in which it is decomposed into the inner-loop estimation of update vectors. We propose outer proximal policy optimization (outer-PPO); a framework wherein these update vectors are applied using an arbitrary gradient-based gradient. Methods are evaluated against an aggressively tuned PPO baseline on Brax, Jumanji and MinAtar environments.
• Score: 17.577317574595206
• License:
• Abstract: Proximal policy optimization (PPO) is a widely-used algorithm for on-policy reinforcement learning. This work offers an alternative perspective of PPO, in which it is decomposed into the inner-loop estimation of update vectors, and the outer-loop application of updates using gradient ascent with unity learning rate. Using this insight we propose outer proximal policy optimization (outer-PPO); a framework wherein these update vectors are applied using an arbitrary gradient-based optimizer. The decoupling of update estimation and update application enabled by outer-PPO highlights several implicit design choices in PPO that we challenge through empirical investigation. In particular we consider non-unity learning rates and momentum applied to the outer loop, and a momentum-bias applied to the inner estimation loop. Methods are evaluated against an aggressively tuned PPO baseline on Brax, Jumanji and MinAtar environments; non-unity learning rates and momentum both achieve statistically significant improvement on Brax and Jumanji, given the same hyperparameter tuning budget.

Related papers

• Accelerated Preference Optimization for Large Language Model Alignment [60.22606527763201]
  Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal tool for aligning large language models (LLMs) with human preferences. Direct Preference Optimization (DPO) formulates RLHF as a policy optimization problem without explicitly estimating the reward function. We propose a general Accelerated Preference Optimization (APO) framework, which unifies many existing preference optimization algorithms.
  arXiv  Detail & Related papers  (2024-10-08T18:51:01Z)
• Efficient Learning of POMDPs with Known Observation Model in Average-Reward Setting [56.92178753201331]
  We propose the Observation-Aware Spectral (OAS) estimation technique, which enables the POMDP parameters to be learned from samples collected using a belief-based policy. We show the consistency of the OAS procedure, and we prove a regret guarantee of order $mathcalO(sqrtT log(T)$ for the proposed OAS-UCRL algorithm.
  arXiv  Detail & Related papers  (2024-10-02T08:46:34Z)
• A dynamical clipping approach with task feedback for Proximal Policy Optimization [29.855219523565786]
  There is no theoretical proof that the optimal PPO clipping bound remains consistent throughout the entire training process. Past studies have aimed to dynamically adjust PPO clipping bound to enhance PPO's performance. We propose Preference based Proximal Policy Optimization (Pb-PPO) to better reflect the preference (maximizing Return) of reinforcement learning tasks.
  arXiv  Detail & Related papers  (2023-12-12T06:35:56Z)
• Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning [13.652106087606471]
  This paper proposes an algorithm that aims to improve generalization for reinforcement learning agents by removing overfitting to confounding features. A policy network updates its parameters to minimize the effect of such perturbations, thus staying robust while maximizing the expected future reward. We evaluate our approach on Procgen and Distracting Control Suite for generalization and sample efficiency.
  arXiv  Detail & Related papers  (2023-08-29T18:17:35Z)
• Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning [139.53668999720605]
  We present a multi-agent PPO algorithm in which the local policy of each agent is updated similarly to vanilla PPO. We prove that with standard regularity conditions on the Markov game and problem-dependent quantities, our algorithm converges to the globally optimal policy at a sublinear rate.
  arXiv  Detail & Related papers  (2023-05-08T16:20:03Z)
• Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees [56.848265937921354]
  Inverse reinforcement learning (IRL) aims to recover the reward function and the associated optimal policy. Many algorithms for IRL have an inherently nested structure. We develop a novel single-loop algorithm for IRL that does not compromise reward estimation accuracy.
  arXiv  Detail & Related papers  (2022-10-04T17:13:45Z)
• A Parametric Class of Approximate Gradient Updates for Policy Optimization [47.69337420768319]
  We develop a unified perspective that re-expresses the underlying updates in terms of a limited choice of gradient form and scaling function. We obtain novel yet well motivated updates that generalize existing algorithms in a way that can deliver benefits both in terms of convergence speed and final result quality.
  arXiv  Detail & Related papers  (2022-06-17T01:28:38Z)
• You May Not Need Ratio Clipping in PPO [117.03368180633463]
  Proximal Policy Optimization (PPO) methods learn a policy by iteratively performing multiple mini-batch optimization epochs of a surrogate objective with one set of sampled data. Ratio clipping PPO is a popular variant that clips the probability ratios between the target policy and the policy used to collect samples. We show in this paper that such ratio clipping may not be a good option as it can fail to effectively bound the ratios. We show that ESPO can be easily scaled up to distributed training with many workers, delivering strong performance as well.
  arXiv  Detail & Related papers  (2022-01-31T20:26:56Z)
• Near Optimal Policy Optimization via REPS [33.992374484681704]
  emphrelative entropy policy search (REPS) has demonstrated successful policy learning on a number of simulated and real-world robotic domains. There exist no guarantees on REPS's performance when using gradient-based solvers. We introduce a technique that uses emphgenerative access to the underlying decision process to compute parameter updates that maintain favorable convergence to the optimal regularized policy.
  arXiv  Detail & Related papers  (2021-03-17T16:22:59Z)
• Proximal Policy Optimization Smoothed Algorithm [0.0]
  We present a PPO variant, named Proximal Policy Optimization Smooth Algorithm (PPOS) Its critical improvement is the use of a functional clipping method instead of a flat clipping method. We show that it outperforms the latest PPO variants on both performance and stability in challenging continuous control tasks.
  arXiv  Detail & Related papers  (2020-12-04T07:43:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.