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This paper proposes a stochastic optimal relaxed control methodology based on reinforcement learning (RL) for solving the automatic generation control (AGC) under NERC's control performance standards (CPS). The multi-step Q (λ) learning algorithm is introduced to effectively tackle the long time-delay control loop for AGC thermal plants in non-Markov environment. The moving averages of CPS1/ACE are adopted as the state feedback input, and the CPS control and relaxed control objectives are formulated as multi-criteria reward function via linear weighted aggregate method. This optimal AGC strategy provides a customized platform for interactive self-learning rules to maximize the long-run discounted reward. Statistical experiments show that the RL theory based Q (λ) controllers can effectively enhance the robustness and dynamic performance of AGC systems, and reduce the number of pulses and pulse …