lilucse/SparseNetwork4DRL
[ICML 2025 oral] Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning
This is a codebase for deep reinforcement learning researchers and practitioners. It takes a reinforcement learning environment configuration and outputs a trained agent that can perform actions within that environment more efficiently, especially for complex tasks. It's for those working on training AI agents for tasks like robotics or control systems.
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Use this if you are a researcher or advanced practitioner working with deep reinforcement learning and want to explore how network sparsity can improve training efficiency and scalability for your agents.
Not ideal if you are new to deep reinforcement learning or looking for a high-level library to quickly implement standard reinforcement learning algorithms without diving into network architecture details.
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Jun 05, 2025
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