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Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
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This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions. We also introduce a suite of eight tasks that combine these three properties, and show that R2D3 can solve several of the tasks where other state of the art methods (both with and without demonstrations) fail to see even a single successful trajectory after tens of billions of steps of exploration.
Forward citations
Cited by 3 Pith papers
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Reinforcement Learning with Physics-Informed Symbolic Program Priors for Zero-Shot Wireless Indoor Navigation
Physics priors written as symbolic programs constrain a PPO agent's action choices, yielding better zero-shot wireless indoor navigation and 26%+ training-time savings on Gibson maps.
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Reinforcement Learning via Implicit Imitation Guidance
A reinforcement learning method that learns a state-dependent covariance from expert-policy action differences and uses it as exploration noise, improving sample efficiency on sparse-reward continuous control tasks.
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Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning
MoE-GUIDE guides RL exploration by rewarding states that a mixture of autoencoders, trained on sparse state-only expert demonstrations, considers similar to expert data.
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