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Found 4 papers, 1 papers with code
On Learning Action Costs from Input Plans
Most of the work on learning action models focus on learning the actions' dynamics from input plans.
On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning
The use of Potential Based Reward Shaping (PBRS) has shown great promise in the ongoing research effort to tackle sample inefficiency in Reinforcement Learning (RL).
Time-Variant Variational Transfer for Value Functions
In most of the transfer learning approaches to reinforcement learning (RL) the distribution over the tasks is assumed to be stationary.
Stochastic Variance-Reduced Policy Gradient
In this paper, we propose a novel reinforcement- learning algorithm consisting in a stochastic variance-reduced version of policy gradient for solving Markov Decision Processes (MDPs).
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