One post tagged with "dqn"

📌 Key Takeaways (TL;DR)

• Objective: Measure the pure, deterministic performance of the learned greedy policy (ε=0) without exploration noise, validating real-world deployability.
• Methodology: Implemented evaluate_qlearning.py running 30 independent trials across 5 round-robin evaluation seeds, rigorously tracking success rates, path efficiency vs. theoretical shortest routes, and obstacle near-miss counts.
• Result: Tabular Q-Learning attained a 93.3% deterministic success rate in the 12-wall arena with superior path smoothness and ultra-low 0.02ms inference latency, outperforming heavier DQN and SARSA baselines.