A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning

• URL: http://arxiv.org/abs/2201.00129v2
• Date: Wed, 27 Apr 2022 15:30:26 GMT
• Title: A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning
• Authors: Yuxing Wang, Tiantian Zhang, Yongzhe Chang, Bin Liang, Xueqian Wang, Bo Yuan
• Abstract summary: In this work, we propose Surrogate-assisted Controller (SC), a novel and efficient module that can be integrated into existing frameworks. The key challenge is to prevent the optimization process from being misled by the possible false minima introduced by the surrogate. Experiments on six continuous control tasks from the OpenAI Gym platform show that SC can not only significantly reduce the cost of fitness evaluations, but also boost the performance of the original hybrid frameworks.
• Score: 14.128178683323108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of Reinforcement Learning (RL) and Evolutionary Algorithms (EAs) aims at simultaneously exploiting the sample efficiency as well as the diversity and robustness of the two paradigms. Recently, hybrid learning frameworks based on this principle have achieved great success in various challenging robot control tasks. However, in these methods, policies from the genetic population are evaluated via interactions with the real environments, limiting their applicability in computationally expensive problems. In this work, we propose Surrogate-assisted Controller (SC), a novel and efficient module that can be integrated into existing frameworks to alleviate the computational burden of EAs by partially replacing the expensive policy evaluation. The key challenge in applying this module is to prevent the optimization process from being misled by the possible false minima introduced by the surrogate. To address this issue, we present two strategies for SC to control the workflow of hybrid frameworks. Experiments on six continuous control tasks from the OpenAI Gym platform show that SC can not only significantly reduce the cost of fitness evaluations, but also boost the performance of the original hybrid frameworks with collaborative learning and evolutionary processes.

Related papers

• Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution [92.84441068115517]
  Investigate-Consolidate-Exploit (ICE) is a novel strategy for enhancing the adaptability and flexibility of AI agents. ICE promotes the transfer of knowledge between tasks for genuine self-evolution. Our experiments on the XAgent framework demonstrate ICE's effectiveness, reducing API calls by as much as 80%.
  arXiv  Detail & Related papers  (2024-01-25T07:47:49Z)
• Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance [15.498559530889839]
  In this paper, we put forward a decentralized learning based Alternative Multi-Agent Proximal Policy Optimization (IA-MAPPO) algorithm to execute the pursuit avoidance task in well-formed swarm. We utilize imitation learning to decentralize the formation controller, so as to reduce the communication overheads and enhance the scalability. The simulation results validate the effectiveness of IA-MAPPO and extensive ablation experiments further show the performance comparable to a centralized solution with significant decrease in communication overheads.
  arXiv  Detail & Related papers  (2023-11-06T06:58:16Z)
• REX: Rapid Exploration and eXploitation for AI Agents [103.68453326880456]
  We propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX. REX introduces an additional layer of rewards and integrates concepts similar to Upper Confidence Bound (UCB) scores, leading to more robust and efficient AI agent performance.
  arXiv  Detail & Related papers  (2023-07-18T04:26:33Z)
• Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning [56.26889258704261]
  We propose a novel "disentangled" decision-making method, Semantically Aligned task decomposition in MARL (SAMA) SAMA prompts pretrained language models with chain-of-thought that can suggest potential goals, provide suitable goal decomposition and subgoal allocation as well as self-reflection-based replanning. SAMA demonstrates considerable advantages in sample efficiency compared to state-of-the-art ASG methods.
  arXiv  Detail & Related papers  (2023-05-18T10:37:54Z)
• Human-Inspired Framework to Accelerate Reinforcement Learning [1.6317061277457001]
  Reinforcement learning (RL) is crucial for data science decision-making but suffers from sample inefficiency. This paper introduces a novel human-inspired framework to enhance RL algorithm sample efficiency.
  arXiv  Detail & Related papers  (2023-02-28T13:15:04Z)
• Safety Correction from Baseline: Towards the Risk-aware Policy in Robotics via Dual-agent Reinforcement Learning [64.11013095004786]
  We propose a dual-agent safe reinforcement learning strategy consisting of a baseline and a safe agent. Such a decoupled framework enables high flexibility, data efficiency and risk-awareness for RL-based control. The proposed method outperforms the state-of-the-art safe RL algorithms on difficult robot locomotion and manipulation tasks.
  arXiv  Detail & Related papers  (2022-12-14T03:11:25Z)
• TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control [1.101002667958165]
  Reinforcement Learning (RL) wherein an agent learns the policy by directly interacting with the environment, offers a potential alternative in this context. RL frameworks with actor-critic architecture have recently become popular for controlling systems where state and action spaces are continuous. It has been shown that an ensemble of actor and critic networks further helps the agent learn better policies due to the enhanced exploration due to simultaneous policy learning.
  arXiv  Detail & Related papers  (2022-04-22T13:00:51Z)
• Safe-Critical Modular Deep Reinforcement Learning with Temporal Logic through Gaussian Processes and Control Barrier Functions [3.5897534810405403]
  Reinforcement learning (RL) is a promising approach and has limited success towards real-world applications. In this paper, we propose a learning-based control framework consisting of several aspects. We show such an ECBF-based modular deep RL algorithm achieves near-perfect success rates and guard safety with a high probability.
  arXiv  Detail & Related papers  (2021-09-07T00:51:12Z)
• Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT [106.83952081124195]
  Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. We propose an adaptive ADMM (asI-ADMM) algorithm and apply it to decentralized RL with edge-computing-empowered IIoT networks. Experiment results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability, and can well adapt to complex IoT environments.
  arXiv  Detail & Related papers  (2021-06-30T16:49:07Z)
• Constrained Model-Free Reinforcement Learning for Process Optimization [0.0]
  Reinforcement learning (RL) is a control approach that can handle nonlinear optimal control problems. Despite the promise exhibited, RL has yet to see marked translation to industrial practice. We propose an 'oracle'-assisted constrained Q-learning algorithm that guarantees the satisfaction of joint chance constraints with a high probability.
  arXiv  Detail & Related papers  (2020-11-16T13:16:22Z)
• Strictly Batch Imitation Learning by Energy-based Distribution Matching [104.33286163090179]
  Consider learning a policy purely on the basis of demonstrated behavior -- that is, with no access to reinforcement signals, no knowledge of transition dynamics, and no further interaction with the environment. One solution is simply to retrofit existing algorithms for apprenticeship learning to work in the offline setting. But such an approach leans heavily on off-policy evaluation or offline model estimation, and can be indirect and inefficient. We argue that a good solution should be able to explicitly parameterize a policy, implicitly learn from rollout dynamics, and operate in an entirely offline fashion.
  arXiv  Detail & Related papers  (2020-06-25T03:27:59Z)

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.