This paper we present a defogger, a model that learns to predict future hidden information from partial observations. We formulate this model in the context of forward modeling and leverage spatial and sequential constraints and correlations via convolutional neural networks and long short-term memory networks, respectively. We evaluate our approach on a large dataset of human games of StarCraft: Brood War, a real-time strategy video game. Our models consistently beat strong rule-based baselines and qualitatively produce sensible future game states. We consider the problem of joint state estimation and next-state prediction in partially observable environments with complex dynamics. We take as a concrete example the problem of defogging in the real-time strategy (RTS) video game StarCraft, which we define as predicting the features of the game state that are hidden to the player.Forward modeling, the prediction of what is going to happen next, is a core enabler both for reactive control and for longer term planning. Many researchers are attempting to build and create algorithms that are able to model the future, especially in next frame video prediction and robotic planning BID9 BID0 One particular difficulty of forward modeling is to deal with the uncertainty of making a prediction with only a partial model and a partial view of the world. BID4 ; BID3 .In RTS games such as StarCraft, players must build an economy and control agents, called units, on a 2 dimensional grid to overcome their opponents. Several inherent limitations of any real-world setting are made explicit in such RTS games. First, by the "fog of war" which only allows players to see the surroundings of their own units and are thus unable to fully access the true game state. Second , the low-level dynamics are extremely complex, because several hundreds of agents interact together. However , there is an implicit spatio-temporal structure that makes long-term reasonning depend mostly on lower-resolution abstractions that can be obtained by averaging fine-grained characteristics over time and space. This poses a challenge for both human and computer players alike and predicting hidden information is a key step towards efficient planning in such environments, In this paper, as a first step towards learning a fully-featured forward model of the environment, the task we propose is to uncover hidden information and to predict the next state from observational data. We present a comprehensive analysis of a StarCraft Defogger, which predict features of the game at different levels of granularity: global features of the game such as the buildings of the opponent, and local features such as army density averaged by regions. Starting from a map of the environment subsampled in time and space, we propose a deep architecture of stacked long short-term memory cells applied convolutionally in an encoder-decoder architecture to predict the full state at different spatial resolutions. Individual layers of convolutional LSTMs encode the dynamics of the local features, and are aggregated in subsequent layers to model lower-resolution movements and global features. Trained on a large dataset of human replays BID8 , the model significantly outperforms strong rule-based baselines on several metrics.
This paper presents a defogger, a model that learns to predict future hidden information from partial observations, applied to a StarCraft dataset.
{"src": "scitldr-AIC_train", "paper_id": "B1nxTzbRZ"}