This work presents an approach to transition from policies learned from human demonstrations to reinforcement learning using an actor critic architecture. Our approach, the psycho-flearning, integrates learning from multiple human interaction modalities to reinforcement learning. The key ideas we present in this paper are first a pre-training phase. We load previous human demonstrations and train the actor in critic networks without interacting with the environment. Second, a combined loss function. We combine it in a single update supervising reinforcement learning loss functions to the radiation. And third, a component analysis to investigate how the pre-training phase and each loss component contributed to the overall policy training. For our approach we first collect human demonstrations for the task we want to solve. In this case, open a iGim Lunarlander continues and a custom quad rotor landing task implemented in Microsoft AirSIM. Then we store all these trajectories in a separate expert buffer. During the pre-training phase, the agents uses the expert data to train both actor and critic networks without interacting with the environment. The combined loss function computes the one step return Q loss, the actor Q loss and the behavior coding loss for the expert samples. Then, during the training phase, the agent interacts with the environment to collect new trajectories which are stored in a separate agent buffer so the expert samples are never discarded. The training process continues using the combined loss, now sampling from both buffers with the fixed ratio of 25% of expert data and 75% of agent data. This approach leads to policies that corrects for inefficiencies in the demonstrated trajectories. It also generalizes to small variations in the environment. For example, for the quad rotor landing task, policies stranded with demonstrations from windy episodes are able to generalize two cases without it. As baselines, we compared our method the cycle of learning against the standard behavior coding, the DDPG reinforcement learning algorithm and the ABG, which also combines demonstration data to reinforcement learning. These plots show how the cycle of learning outperforms the baselines in terms of training rewards when evaluated in the lunar lander continuous environment. For both, the instance parts reward cases and also in the custom quad rotor landing task, which uses a sparse reward function. Our component analysis showed that the pre-training phase enabled agents to start interacting with the environment with an already a reasonable policy, which is essential in real-world robotics applications. It also shows that after the pre-training phase, the behavior coding loss component leads to more stable policies. Finally, we showed that sampling from both buffers with a fixed ratio out performance prioritized experience replay for sparse rewards scenarios. In summary, this work enables transition from behavior coding to reinforcement learning without performance degradation and improves reinforcement learning in terms of overall performance and training time for both dance and sparse rewards scenarios. Thank you very much for your time and feel free to reach us out with any questions.