Exploration in approximate hyper-state space for meta reinforcement learning
To rapidly learn a new task, it is often essential for agents to explore efficiently - especially when performance matters from the first timestep. One way to learn such behaviour is via meta-learning. Many existing methods however rely on dense rewards for meta-training, and can fail catastrophical...
| Main Authors: | , , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
PMLR
2021
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