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Found 7 papers, 5 papers with code
Score vs. Winrate in Score-Based Games: which Reward for Reinforcement Learning?
In the last years, the DeepMind algorithm AlphaZero has become the state of the art to efficiently tackle perfect information two-player zero-sum games with a win/lose outcome.
Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects
In the last few years, the scientific community showed a remarkable and increasing interest towards 3D Virtual Environments, training and testing Machine Learning-based models in realistic virtual worlds.
Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic
Assuming that each eMBB codeword can tolerate a certain limited amount of puncturing beyond which is in outage, we show that the policy devised by the DRL agent never violates the latency requirement of URLLC traffic and, at the same time, manages to keep the number of eMBB codewords in outage at minimum levels, when compared to other state-of-the-art schemes.
Generate and Revise: Reinforcement Learning in Neural Poetry
Writers, poets, singers usually do not create their compositions in just one breath.
Pseudo Random Number Generation through Reinforcement Learning and Recurrent Neural Networks
This paper proposes a Reinforcement Learning (RL) approach to the task of generating PRNGs from scratch by learning a policy to solve a partially observable Markov Decision Process (MDP), where the full state is the period of the generated sequence and the observation at each time step is the last sequence of bits appended to such state.
SAILenv: Learning in Virtual Visual Environments Made Simple
Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers and others, showed a growing interest in 3D simulators as a mean to artificially create experimental settings that are very close to those in the real world.
Pseudo Random Number Generation: a Reinforcement Learning approach
In this context, N is the length of the period of the generated sequence, and the policy is iteratively improved using the average value of an appropriate test suite run over that period.
