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Found 3 papers, 1 papers with code
Conservative Exploration for Policy Optimization via Off-Policy Policy Evaluation
A precondition for the deployment of a Reinforcement Learning agent to a real-world system is to provide guarantees on the learning process.
The guide and the explorer: smart agents for resource-limited iterated batch reinforcement learning
Iterated batch reinforcement learning (RL) is a growing subfield fueled by the demand from systems engineers for intelligent control solutions that they can apply within their technical and organizational constraints.
Conservative Optimistic Policy Optimization via Multiple Importance Sampling
Reinforcement Learning (RL) has been able to solve hard problems such as playing Atari games or solving the game of Go, with a unified approach.
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