How long does an agent need to become autonomous ?

The time required for an agent to become autonomous in Deep Reinforcement Learning (DRL) depends on several factors, including the complexity of the task, the quality of the training data, and the performance of the learning algorithm. Training an agent in DRL can be a time-consuming process, often requiring millions of iterations before the agent can achieve a satisfactory level of performance. The time required for training can vary widely depending on the complexity of the task and the size of the agent’s neural network. In some cases, training an agent can take days, weeks, or even months, depending on the available computational resources. In addition to the time required for training, the agent’s ability to become autonomous also depends on the quality of the training data. If the training data is noisy, incomplete, or biased, the agent’s performance may be suboptimal, and it may take longer for the agent to become fully autonomous. The performance of the learning algorithm used to train the agent also plays a crucial role in determining the time required for the agent to become autonomous. More advanced and efficient algorithms can often achieve better results in less time, but may also require more computational resources.