Mamba and PPO achieve superior safety in spacecraft control

A new research paper explores the effectiveness of various recurrent neural network architectures and reinforcement learning algorithms for adaptive safety-critical control in spacecraft proximity operations. The study specifically compares Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Selective State Space Model (Mamba) networks, alongside Proximal Policy Optimization (PPO) and Soft Actor Critic (SAC) training algorithms. Results show that Mamba, when paired with PPO, demonstrated superior performance in task completion, safety, and fuel efficiency, even in adversarial scenarios. AI

IMPACT Demonstrates potential for advanced AI control systems in safety-critical aerospace applications.

RANK_REASON Academic paper published on arXiv detailing a novel application of meta-reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• gated recurrent unit
• long short-term memory
• Mamba
• Minduli Wijayatunga
• Proximal Policy Optimization
• Selective State Space Model
• SOFT ACTOR-CRITIC REINFORCEMENT LEARNING FOR ROBOTIC MANIPULATOR WITH HINDSIGHT EXPERIENCE REPLAY

• paper
• safety
• infra

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