Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design

• URL: http://arxiv.org/abs/2310.02782v1
• Date: Wed, 4 Oct 2023 12:52:56 GMT
• Title: Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design
• Abstract summary: We show that it is possible to meta-learn update rules, with the hope of discovering algorithms that can perform well on a wide range of RL tasks. Despite impressive initial results from algorithms such as Learned Policy Gradient (LPG), there remains a gap when these algorithms are applied to unseen environments. In this work, we examine how characteristics of the meta-supervised-training distribution impact the performance of these algorithms.
• Score: 54.39859618450935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The past decade has seen vast progress in deep reinforcement learning (RL) on the back of algorithms manually designed by human researchers. Recently, it has been shown that it is possible to meta-learn update rules, with the hope of discovering algorithms that can perform well on a wide range of RL tasks. Despite impressive initial results from algorithms such as Learned Policy Gradient (LPG), there remains a generalization gap when these algorithms are applied to unseen environments. In this work, we examine how characteristics of the meta-training distribution impact the generalization performance of these algorithms. Motivated by this analysis and building on ideas from Unsupervised Environment Design (UED), we propose a novel approach for automatically generating curricula to maximize the regret of a meta-learned optimizer, in addition to a novel approximation of regret, which we name algorithmic regret (AR). The result is our method, General RL Optimizers Obtained Via Environment Design (GROOVE). In a series of experiments, we show that GROOVE achieves superior generalization to LPG, and evaluate AR against baseline metrics from UED, identifying it as a critical component of environment design in this setting. We believe this approach is a step towards the discovery of truly general RL algorithms, capable of solving a wide range of real-world environments.

Related papers

• Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs [53.412166189410904]
  We use supervised learning as a computational proxy to establish a hierarchy of commonly adopted RL oracles under low-rank Markov Decision Processes (MDPs)<n>Motivated by this observation, we propose a novel optimistic actor-critic algorithm that relies solely on the policy evaluation oracle.<n>We prove that our algorithm outperforms the existing sample complexity guarantees for low-rank MDPs while avoiding computationally expensive planning or optimization oracles commonly assumed in prior works.
  arXiv  Detail & Related papers  (2026-05-02T04:46:54Z)
• Poly-EPO: Training Exploratory Reasoning Models [62.82992914206963]
  We present a framework for post-training language models (LMs) that explicitly encourages optimistic exploration and promotes a synergy between exploration and exploitation.<n>We propose Polychromic Exploratory Policy Optimization (Poly-EPO), which instantiates this framework with an objective that explicitly synergizes exploration and exploitation.
  arXiv  Detail & Related papers  (2026-04-19T22:54:19Z)
• Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations [98.44542103979735]
  First-order gradient descent methods, epitomized by gradient descent (SGD) and Adam, serve as the cornerstone of modern training pipelines.<n>Large-scale model training, stringent privacy requirements, and distributed learning paradigms expose critical limitations in these conventional approaches regarding privacy protection and memory efficiency.<n>We retrospectively analyze the evolutionary trajectory of deep learning optimization algorithms and present a comprehensive empirical evaluation of mainstreams across diverse model architectures and training scenarios.<n>We distill key emerging trends and fundamental design trade-offs, pinpointing promising directions for future research.
  arXiv  Detail & Related papers  (2026-04-14T17:01:36Z)
• Landscape-aware Automated Algorithm Design: An Efficient Framework for Real-world Optimization [32.203665659052845]
  Large Language Models (LLMs) have opened new frontiers in automated algorithm design.<n>LLMs require extensive evaluation of the target problem to guide the search process.<n>This research proposes an innovative and efficient framework that decouples algorithm discovery from high-cost evaluation.
  arXiv  Detail & Related papers  (2026-02-04T13:18:45Z)
• Position: We Need An Algorithmic Understanding of Generative AI [7.425924654036041]
  This position paper proposes AlgEval: a framework for systematic research into the algorithms that LLMs learn and use.<n>AlgEval aims to uncover algorithmic primitives, reflected in latent representations, attention, and inference-time compute, and their algorithmic composition to solve task-specific problems.
  arXiv  Detail & Related papers  (2025-07-10T08:38:47Z)
• Surpassing legacy approaches to PWR core reload optimization with single-objective Reinforcement learning [0.0]
  We have developed methods based on Deep Reinforcement Learning (DRL) for both single- and multi-objective optimization. In this paper, we demonstrate the advantage of our RL-based approach, specifically using Proximal Policy Optimization (PPO) PPO adapts its search capability via a policy with learnable weights, allowing it to function as both a global and local search method.
  arXiv  Detail & Related papers  (2024-02-16T19:35:58Z)
• Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities [63.258517066104446]
  Reinforcement learning integrated as a component in the evolutionary algorithm has demonstrated superior performance in recent years. We discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. In the applications of RL-EA section, we also demonstrate the excellent performance of RL-EA on several benchmarks and a range of public datasets.
  arXiv  Detail & Related papers  (2023-08-25T15:06:05Z)
• Discovered Policy Optimisation [17.458523575470384]
  We explore the Mirror Learning space by meta-learning a "drift" function. We refer to the immediate result as Learnt Policy optimisation (LPO) By analysing LPO we gain original insights into policy optimisation which we use to formulate a novel, closed-form RL algorithm, Discovered Policy optimisation (DPO)
  arXiv  Detail & Related papers  (2022-10-11T17:32:11Z)
• Identifying Co-Adaptation of Algorithmic and Implementational Innovations in Deep Reinforcement Learning: A Taxonomy and Case Study of Inference-based Algorithms [15.338931971492288]
  We focus on a series of inference-based actor-critic algorithms to decouple their algorithmic innovations and implementation decisions. We identify substantial performance drops whenever implementation details are mismatched for algorithmic choices. Results show which implementation details are co-adapted and co-evolved with algorithms.
  arXiv  Detail & Related papers  (2021-03-31T17:55:20Z)
• Evolving Reinforcement Learning Algorithms [186.62294652057062]
  We propose a method for meta-learning reinforcement learning algorithms. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. We highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games.
  arXiv  Detail & Related papers  (2021-01-08T18:55:07Z)
• Discovering Reinforcement Learning Algorithms [53.72358280495428]
  Reinforcement learning algorithms update an agent's parameters according to one of several possible rules. This paper introduces a new meta-learning approach that discovers an entire update rule. It includes both 'what to predict' (e.g. value functions) and 'how to learn from it' by interacting with a set of environments.
  arXiv  Detail & Related papers  (2020-07-17T07:38:39Z)
• A Brief Look at Generalization in Visual Meta-Reinforcement Learning [56.50123642237106]
  We evaluate the generalization performance of meta-reinforcement learning algorithms. We find that these algorithms can display strong overfitting when they are evaluated on challenging tasks.
  arXiv  Detail & Related papers  (2020-06-12T15:17:17Z)
• Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO [90.90009491366273]
  We study the roots of algorithmic progress in deep policy gradient algorithms through a case study on two popular algorithms. Specifically, we investigate the consequences of "code-level optimizations:" Our results show that they (a) are responsible for most of PPO's gain in cumulative reward over TRPO, and (b) fundamentally change how RL methods function.
  arXiv  Detail & Related papers  (2020-05-25T16:24:59Z)

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.