Unverified50% confidenceFactExact time
Q-learning's update target is the immediate reward plus the discounted maximum Q value of the next state, so regardless of what behavior policy the agent actually follows, it is approximating the optimal action-value function.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedQ-learning is off-policy learning, which updates Q-values using a greedy policy but can employ different policies such as ε-greedy during exploration.79% similarUnverifiedDQN的理论基础是Q-learning，一种无模型强化学习算法，核心是学习Q函数Q(s,a)表示状态s下执行动作a的期望累积折扣奖励76% similarUnverifiedQ-Learning is an off-policy method that assumes optimal future actions when updating Q-values; SARSA is an on-policy method that updates Q-values based on the actions actually taken.73% similarUnverifiedQ-FLAIR is an adaptive scheme that lets a quantum feature map circuit grow gate by gate, checking at each step whether the training loss improves and stopping once further growth no longer brings benefit.71% similarUnverifiedQ-learning、DQN 等算法本质上都是在近似求解贝尔曼最优方程70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1028363API
curl https://kongchang.com/api/v1/knowledge/claims/1028363MCP
get_claim(id=1028363)