2 links tagged with all of: reinforcement learning + simulation
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The author ported a Python simulation of weakly electric fish to C and optimized it to run at one million steps per second—a 250x speedup over the original. This enabled training RL policies for the fish in under 15 seconds while maintaining biological accuracy, demonstrating how performance engineering unlocks rapid experimentation in complex simulations.
- Ported a weakly electric fish RL simulation from Python to C, boosting speed from 4,300 to 1.85M steps/sec (250x), enabling full policy training in under 10-15 seconds on a single GPU.
- Exploiting the biological detail that sensory receptors have limited range (4-10cm vs 100cm) let them skip most fish-to-fish field calculations, fixing the main bottleneck.
- Switching from double- to single-precision floats alone gave a 30% speedup.
- Shows that fast iteration—borrowed from autonomous driving RL practices—can work even in messy, physics-heavy biological simulations, and the whole thing is only 1,200 lines of open-sourced C code.
+ weakly electric fish
reinforcement learning
+ performance engineering
simulation
+ optimization
+ biology
This paper introduces Qwen-AgentWorld, large language models trained to simulate agentic environments across seven domains using over 10 million interaction trajectories. The authors detail a three-stage training pipeline (CPT, SFT, RL) and evaluate on AgentWorldBench, showing superior simulation fidelity. They also demonstrate its use as both a standalone simulator for RL and a warm-up step that boosts downstream agent performance.
- Qwen-AgentWorld's 397B model held correct object-grasp sequences over 25 reasoning steps in a robotics task without losing context, beating existing simulators on state accuracy and reasoning-chain length.
- RL agents trained inside Qwen-AgentWorld's simulated environments outperformed agents trained only in the real world by 15–20%.
- Using the world model's weights to warm-start a unified agent boosted performance across seven benchmarks and sometimes cut required training runs in half.