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Alexander Neitz

Alumni

Publications

Towards Jumpy Planning
Akilesh
Suriya Singh
Anirudh Goyal
Alexander Neitz
Aaron Courville
Towards Jumpy Planning
Akilesh
Suriya Singh
Anirudh Goyal
Alexander Neitz
Aaron Courville
Model-free reinforcement learning (RL) is a powerful paradigm for learning complex tasks but suffers from high sample inefficiency as well a… (voir plus)s ignorance of the environment dynamics. On the other hand, a model-based RL agent learns dynamical causal models of the environment and uses them to plan. However, using a model at the scale of time-steps (usually tens of milliseconds) is mostly unfeasible in practice due to compounding prediction errors and computational requirements for making vast numbers of model queries during the planning process. We propose to use a modelbased planner together with a goal-conditioned policy trained with model-free learning. We use a model-based planner that operates at higher levels of abstraction i.e., decision states and use modelfree RL between the decision states. We validate our approach in terms of transfer and generalization performance and show that it leads to improvement over model-based planner that jumps to states that are fixed timesteps ahead.
2019-01-01
(publié)
www.semanticscholar.org