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state_space_model [2016/12/09 20:12]
127.0.0.1 external edit
state_space_model [2018/08/19 23:05] (current)
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 sets by a large margin, while achieving comparable performances to competing sets by a large margin, while achieving comparable performances to competing
 methods on polyphonic music modeling. methods on polyphonic music modeling.
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 +https://​openreview.net/​forum?​id=HJw8fAgA- Learning Dynamic State Abstractions for Model-Based Reinforcement Learning
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 +https://​arxiv.org/​abs/​1802.03006v1 Learning and Querying Fast Generative Models for Reinforcement Learning
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 +A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed generative models that learn and operate on compact state representations,​ so-called state-space models, substantially reduce the computational costs for predicting outcomes of sequences of actions. Extensive experiments establish that state-space models accurately capture the dynamics of Atari games from the Arcade Learning Environment from raw pixels. The computational speed-up of state-space models while maintaining high accuracy makes their application in RL feasible: We demonstrate that agents which query these models for decision making outperform strong model-free baselines on the game MSPACMAN, demonstrating the potential of using learned environment models for planning.
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 +https://​arxiv.org/​abs/​1807.11929v1 Egocentric Spatial Memory
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 +https://​www.nature.com/​articles/​s41586-018-0102-6 Vector-based navigation using grid-like representations in artificial agents