Collaborative Evolutionary Reinforcement Learning

Abstract

Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically struggle with achieving effective exploration and are extremely sensitive to the choice of hyperparameters. One reason is that most approaches use a noisy version of their operating policy to explore - thereby limiting the range of exploration. In this paper, we introduce Collaborative Evolutionary Reinforcement Learning (CERL), a scalable framework that comprises a portfolio of policies that simultaneously explore and exploit diverse regions of the solution space. A collection of learners - typically proven algorithms like TD3 - optimize over varying time-horizons leading to this diverse portfolio. All learners contribute to and use a shared replay buffer to achieve greater sample efficiency. Computational resources are dynamically distributed to favor the best learners as a form of online algorithm selection. Neuroevolution binds this entire

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keykhadka2019collaborative

Related papers

• Evolution-guided Policy Gradient In Reinforcement Learning (2018)0.00
• CEM-RL: Combining Evolutionary And Gradient-based Methods For Policy Search (2018)0.00
• Centralized Cooperative Exploration Policy For Continuous Control Tasks (2023)0.00
• Evolutionary Reinforcement Learning Via Cooperative Coevolutionary Negatively Correlated Search (2020)9.92
• Erl-re\(^2\): Efficient Evolutionary Reinforcement Learning With Shared State Representation And Individual Policy Representation (2022)0.00
• Evolutionary Reinforcement Learning: A Survey (2023)13.93
• Combining Evolution And Deep Reinforcement Learning For Policy Search: A Survey (2022)12.25
• Neuroevolution Is A Competitive Alternative To Reinforcement Learning For Skill Discovery (2022)0.00