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Take a look at another work that explains a way we can use AI to play games. The thing is that this one focuses on curiosity. How can a machine be curious? In this case, curiosity is defined by the AI’s ability to predict the results of its own actions. And this is big because the AI has the tools to acquire skills that don’t seem necessary now but might be in the future.
First, let’s start by check it out a video on the paper by Two Minute Papers:
In many real-world scenarios, rewards extrinsic to the agent are extremely sparse or absent altogether. In such cases, curiosity can serve as an intrinsic reward signal to enable the agent to explore its environment and learn skills that might be useful later in its life. We formulate curiosity as the error in an agent’s ability to predict the consequence of its own actions in a visual feature space learned by a self-supervised inverse dynamics model. Our formulation scales to high-dimensional continuous state spaces, like images, bypasses the difficulties of directly predicting pixels, and, critically, ignores the aspects of the environment that cannot affect the agent. The proposed approach is evaluated in two environments: VizDoomand Super Mario Bros. Three broad settings are investigated: 1) sparse extrinsic reward, where curiosity allows for far fewer interactions with the environment to reach the goal; 2) exploration with no extrinsic reward, where curiosity pushes the agent to explore more efficiently; and 3) generalization to unseen scenarios (e.g. new levels of the same game) where the knowledge gained from earlier experience helps the agent explore new places much faster than starting from scratch.
The paper “Curiosity-driven Exploration by Self-supervised Prediction” and its source code can be found here.