An agent that plans on a landmark graph built from a locally accurate value function reaches long-range goals faster and more reliably than model-free baselines in sparse-reward environments.
Composable Planning with Attributes
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abstract
The tasks that an agent will need to solve often are not known during training. However, if the agent knows which properties of the environment are important then, after learning how its actions affect those properties, it may be able to use this knowledge to solve complex tasks without training specifically for them. Towards this end, we consider a setup in which an environment is augmented with a set of user defined attributes that parameterize the features of interest. We propose a method that learns a policy for transitioning between "nearby" sets of attributes, and maintains a graph of possible transitions. Given a task at test time that can be expressed in terms of a target set of attributes, and a current state, our model infers the attributes of the current state and searches over paths through attribute space to get a high level plan, and then uses its low level policy to execute the plan. We show in 3D block stacking, grid-world games, and StarCraft that our model is able to generalize to longer, more complex tasks at test time by composing simpler learned policies.
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cs.LG 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Mapping State Space using Landmarks for Universal Goal Reaching
An agent that plans on a landmark graph built from a locally accurate value function reaches long-range goals faster and more reliably than model-free baselines in sparse-reward environments.