Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation

• URL: http://arxiv.org/abs/2503.18531v1
• Date: Mon, 24 Mar 2025 10:40:03 GMT
• Title: Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation
• Authors: Octi Zhang, Quanquan Peng, Rosario Scalise, Bryon Boots,
• Abstract summary: We propose a framework that includes a reproduction module, similar to natural species reproduction, balancing diversity and specialization.<n>By integrating RL, imitation learning (IL), and a coevolutionary agent-terrain curriculum, our system evolves agents continuously through complex tasks.<n>Our initial experiments show that this method improves exploration efficiency and supports open-ended learning.
• Score: 1.124958340749622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing robotic agents that can perform well in diverse environments while showing a variety of behaviors is a key challenge in AI and robotics. Traditional reinforcement learning (RL) methods often create agents that specialize in narrow tasks, limiting their adaptability and diversity. To overcome this, we propose a preliminary, evolution-inspired framework that includes a reproduction module, similar to natural species reproduction, balancing diversity and specialization. By integrating RL, imitation learning (IL), and a coevolutionary agent-terrain curriculum, our system evolves agents continuously through complex tasks. This approach promotes adaptability, inheritance of useful traits, and continual learning. Agents not only refine inherited skills but also surpass their predecessors. Our initial experiments show that this method improves exploration efficiency and supports open-ended learning, offering a scalable solution where sparse reward coupled with diverse terrain environments induces a multi-task setting.

Related papers

• Evolution with Opponent-Learning Awareness [10.689403855269704]
  We show how large heterogeneous populations of learning agents evolve in normal-form games. We derive a fast, parallelizable implementation of Opponent-Learning Awareness tailored for evolutionary simulations. We demonstrate our approach in simulations of 200,000 agents, evolving in the classic games of Hawk-Dove, Stag-Hunt, and Rock-Paper-Scissors.
  arXiv  Detail & Related papers  (2024-10-22T22:49:04Z)
• RILe: Reinforced Imitation Learning [60.63173816209543]
  RILe is a framework that combines the strengths of imitation learning and inverse reinforcement learning to learn a dense reward function efficiently.<n>Our framework produces high-performing policies in high-dimensional tasks where direct imitation fails to replicate complex behaviors.
  arXiv  Detail & Related papers  (2024-06-12T17:56:31Z)
• A Survey on Self-Evolution of Large Language Models [116.54238664264928]
  Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications. To address this issue, self-evolution approaches that enable LLMs to autonomously acquire, refine, and learn from experiences generated by the model itself are rapidly growing.
  arXiv  Detail & Related papers  (2024-04-22T17:43:23Z)
• Evolving Reservoirs for Meta Reinforcement Learning [1.6874375111244329]
  We propose a computational model for studying a mechanism that can enable such a process. At the evolutionary scale, we evolve reservoirs, a family of recurrent neural networks. We employ these evolved reservoirs to facilitate the learning of a behavioral policy through Reinforcement Learning (RL) Our results show that the evolution of reservoirs can improve the learning of diverse challenging tasks.
  arXiv  Detail & Related papers  (2023-12-09T16:11:48Z)
• DARLEI: Deep Accelerated Reinforcement Learning with Evolutionary Intelligence [77.78795329701367]
  We present DARLEI, a framework that combines evolutionary algorithms with parallelized reinforcement learning. We characterize DARLEI's performance under various conditions, revealing factors impacting diversity of evolved morphologies. We hope to extend DARLEI in future work to include interactions between diverse morphologies in richer environments.
  arXiv  Detail & Related papers  (2023-12-08T16:51:10Z)
• RObotic MAnipulation Network (ROMAN) $\unicode{x2013}$ Hybrid Hierarchical Learning for Solving Complex Sequential Tasks [70.69063219750952]
  We present a Hybrid Hierarchical Learning framework, the Robotic Manipulation Network (ROMAN) ROMAN achieves task versatility and robust failure recovery by integrating behavioural cloning, imitation learning, and reinforcement learning. Experimental results show that by orchestrating and activating these specialised manipulation experts, ROMAN generates correct sequential activations for accomplishing long sequences of sophisticated manipulation tasks.
  arXiv  Detail & Related papers  (2023-06-30T20:35:22Z)
• System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning [8.280943341629161]
  We introduce System Neural Diversity (SND): a measure of behavioral heterogeneity in multi-agent systems. We show how SND allows us to measure latent resilience skills acquired by the agents, while other proxies, such as task performance (reward), fail. We demonstrate how this paradigm can be used to bootstrap the exploration phase, finding optimal policies faster.
  arXiv  Detail & Related papers  (2023-05-03T13:58:13Z)
• Evolving Curricula with Regret-Based Environment Design [37.70275057075986]
  We propose to harness the power of evolution in a principled, regret-based curriculum. Our approach seeks to constantly produce levels at the frontier of an agent's capabilities, resulting in curricula that start simple but become increasingly complex.
  arXiv  Detail & Related papers  (2022-03-02T18:40:00Z)
• Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share? [7.489793155793319]
  This work focuses on combining information obtained through intrinsic motivation with the aim of having a more efficient exploration and faster learning. Our results reveal different ways in which a collaborative framework with little additional computational cost can outperform an independent learning process without knowledge sharing.
  arXiv  Detail & Related papers  (2022-02-24T16:15:51Z)
• Autonomous Reinforcement Learning: Formalism and Benchmarking [106.25788536376007]
  Real-world embodied learning, such as that performed by humans and animals, is situated in a continual, non-episodic world. Common benchmark tasks in RL are episodic, with the environment resetting between trials to provide the agent with multiple attempts. This discrepancy presents a major challenge when attempting to take RL algorithms developed for episodic simulated environments and run them on real-world platforms.
  arXiv  Detail & Related papers  (2021-12-17T16:28:06Z)
• Bridging the Imitation Gap by Adaptive Insubordination [88.35564081175642]
  We show that when the teaching agent makes decisions with access to privileged information, this information is marginalized during imitation learning. We propose 'Adaptive Insubordination' (ADVISOR) to address this gap. ADVISOR dynamically weights imitation and reward-based reinforcement learning losses during training, enabling on-the-fly switching between imitation and exploration.
  arXiv  Detail & Related papers  (2020-07-23T17:59:57Z)

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.