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When a gait of a bipedal robot is developed using deep reinforcementlearning, reference trajectories may or may not be used. Each approach has itsadvantages and disadvantages, and the choice of method is up to the controldeveloper. This paper investigates the effect of reference trajectories onlocomotion learning and the resulting gaits. We implemented three gaits of afull-order anthropomorphic robot model with different reward imitation ratios,provided sim-to-sim control policy transfer, and compared the gaits in terms ofrobustness and energy efficiency. In addition, we conducted a qualitativeanalysis of the gaits by interviewing people, since our task was to create anappealing and natural gait for a humanoid robot.  According to the results of the experiments, the most successful approach wasthe one in which the average value of rewards for imitation and adherence tocommand velocity per episode remained balanced throughout the training. Thegait obtained with this method retains naturalness (median of 3.6 according tothe user study) compared to the gait trained with imitation only (median of4.0), while remaining robust close to the gait trained without referencetrajectories.

Benchmarking the Full-Order Model Optimization Based Imitation in the Humanoid Robot Reinforcement Learning Walk