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Found 3 papers, 2 papers with code
Wield: Systematic Reinforcement Learning With Progressive Randomization
Reinforcement learning frameworks have introduced abstractions to implement and execute algorithms at scale.
RLgraph: Modular Computation Graphs for Deep Reinforcement Learning
Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns.
LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations
In this work, we introduce LIFT, an end-to-end software stack for applying deep reinforcement learning to data management tasks.
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