Reproducible and Scalable RL in Gazebo
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Updated
Sep 16, 2026 - C++
Reproducible and Scalable RL in Gazebo
Reinforcement learning for LLM inference scheduling. DQN agent learns to balance throughput, TTFT, latency, and memory pressure vs FIFO/SJF/priority baselines.
GPU-resident, differentiable, vectorized 2D multi-agent simulator built on NVIDIA Warp with zero-copy PyTorch interop.
Vectorized urban multi-agent reinforcement learning for 6G geospatial and radio digital twins.
A Double Deep Q-Network (DDQN) reinforcement learning algorithm to train a Battlesnake agent. Several features are used to enhance the algorithm, including vectorized environments, prioritized experience replay, board canonicalization, self-play, and a Monte Carlo Tree Search.
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