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Mind the Uncertainty: Risk-Aware and Actively Exploring Model-Based Reinforcement Learning
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We introduce a simple but effective method for managing risk in model-based reinforcement learning with trajectory sampling that involves probabilistic safety constraints and balancing of optimism in the face of epistemic uncertainty and pessimism in the face of aleatoric uncertainty of an ensemble of stochastic neural networks.Various experiments indicate that the separation of uncertainties is essential to performing well with data-driven MPC approaches in uncertain and safety-critical control environments.
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Cited by 1 Pith paper
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Belief-Guided Decision Making with Uncertainty Gating in the Game of Go
A disentangled Belief head with uncertainty gating is claimed to replace MCTS correction and enable professional-level search-free Go on consumer GPUs, but the reported experiments do not demonstrate that claim.
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