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Found 6 papers, 0 papers with code
Assessing the Impact of Offshore Wind Siting Strategies on the Design of the European Power System
In addition, two variants of these siting schemes are provided, wherein the number of sites to be selected is specified on a country-by-country basis rather than Europe-wide.
On overfitting and asymptotic bias in batch reinforcement learning with partial observability
This paper provides an analysis of the tradeoff between asymptotic bias (suboptimality with unlimited data) and overfitting (additional suboptimality due to limited data) in the context of reinforcement learning with partial observability.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies
When the discount factor progressively increases up to its final value, we empirically show that it is possible to significantly reduce the number of learning steps.
Benchmarking for Bayesian Reinforcement Learning
In order to enable the comparison of non-anytime algorithms, our methodology also includes a detailed analysis of the computation time requirement of each algorithm.
Simultaneous Perturbation Algorithms for Batch Off-Policy Search
We propose novel policy search algorithms in the context of off-policy, batch mode reinforcement learning (RL) with continuous state and action spaces.
Aggregating Optimistic Planning Trees for Solving Markov Decision Processes
This paper addresses the problem of online planning in Markov Decision Processes using only a generative model.
