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Found 4 papers, 1 papers with code
Learning Goal-Conditioned Value Functions with one-step Path rewards rather than Goal-Rewards
Multi-goal reinforcement learning (MGRL) addresses tasks where the desired goal state can change for every trial.
Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals
We do this by adapting the Floyd-Warshall algorithm for RL and call the adaptation Floyd-Warshall RL (FWRL).
A Critical Investigation of Deep Reinforcement Learning for Navigation
However, when trained and tested on different sets of maps, the algorithm fails to transfer the ability to gather and exploit map-information to unseen maps.
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference
Probabilistic programming languages allow modelers to specify a stochastic process using syntax that resembles modern programming languages.
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