{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Control Policy — AI Glossary","author_name":"Glenn Katrud Solheim","author_url":"https://gaks.ai","width":600,"height":200,"html":"<div style=\"font-family:sans-serif;border:1px solid #e0e0e0;border-radius:8px;padding:16px;max-width:600px;background:#ffffff;color:#111111;\"><p style=\"margin:0 0 4px;font-size:11px;color:#666;\">AI Glossary — gaks.ai</p><h3 style=\"margin:0 0 8px;font-size:16px;\">Control Policy</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">The function or set of rules that determines what actions a robotic or autonomous system takes in response to its current state and environment. In reinforcement learning, the control policy is what the agent learns, mapping observations to actions in a way that maximizes cumulative reward. A well-designed control policy enables reliable, safe behavior across the range of conditions the system will encounter.  See also: reinforcement learning, autonomous agent, reward model.</p><a href=\"https://gaks.ai/glossary/control-policy\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/control-policy →</a></div>"}