Goal-Aware Prediction: Learning to Model What Matters . Goal-Aware Prediction: Learning to Model What Matters. Suraj Nair, Silvio Savarese, Chelsea Finn. Learned dynamics models combined with both planning and policy learning.
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Learned dynamics models combined with both planning and policy learning algorithms have shown promise in enabling artificial agents to learn to perform many diverse.
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Video for the paper Goal-Aware Prediction: Learning to Model What MattersSuraj Nair, Silvio Savarese, Chelsea Finn. ICML 2020.Website: https://sites.google.c...
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%0 Conference Paper %T Goal-Aware Prediction: Learning to Model What Matters %A Suraj Nair %A Silvio Savarese %A Chelsea Finn %B Proceedings of the 37th International Conference on.
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Goal-Aware Prediction: Our proposed method, goal-aware prediction (GAP), encodes both the current state s t and goal s g into a single latent space z t. Samples from the distribution of z t.
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Suraj Nair (Stanford University); Silvio Savarese (Stanford University); Chelsea Finn (Stanford University)
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This paper proposes to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities.
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Learned dynamics models combined with both planning and policy learning algorithms have shown promise in enabling artificial agents to learn to perform many diverse.
Source: cs.stanford.edu
Goal-Aware Prediction: Learning to Model What Matters (GAP) Code for the paper Goal-Aware Prediction: Learning to Model what Matters. Suraj Nair, Silvio Savarese, Chelsea Finn. ICML.
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Goal-Aware Prediction: Learning to Model What Matters ment learning approach. The key insight of this work stems from the idea that the distribution of model errors greatly affects task.
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Note, in the GAP predictions the goal is added back to the predicted goal-state residual. In this case the goal is the rightmost frame. In the top example, we see that GAP more effectively.
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In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a policy, I2As learn to.
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goal-aware prediction: learning to model what matters? – assuming you are one of the creators of the paper and need to deal with your transfer, see the inquiry “my papertalk has been.
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Further, we do so in an entirely self-supervised manner, without the need for a reward function or image labels. We find that our method more effectively models the relevant parts of.
