An adaptive RL-MPC framework uses RL to inform MPPI sampling and aggregates MPPI samples for value estimation, delivering up to 72% higher success rates and 2.1x faster convergence on tasks like race driving and Lunar Lander with obstacles.
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SAT-RTS introduces a pipeline that abstracts high-dimensional RTS sequences into discrete tactical labels and hierarchical visualizations to improve interpretability of AI micromanagement.
MA-DHRL-OM decomposes overlay multicast routing into hierarchical stages with multi-agent RL to improve delay, bandwidth use, and stability over prior methods.
citing papers explorer
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SAT-RTS: A systematic framework for tactical knowledge extraction and visualization-based analysis in real-time strategy games
SAT-RTS introduces a pipeline that abstracts high-dimensional RTS sequences into discrete tactical labels and hierarchical visualizations to improve interpretability of AI micromanagement.
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An Overlay Multicast Routing Method Based on Network Situational Awareness and Hierarchical Multi-Agent Reinforcement Learning
MA-DHRL-OM decomposes overlay multicast routing into hierarchical stages with multi-agent RL to improve delay, bandwidth use, and stability over prior methods.