ReinforcementLearning.jl
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JuliaReinforcementLearning
A reinforcement learning package for Julia
Another interesting direction. - [Reinforcement Learning for Combinatorial Optimization: A Survey](https://arxiv.org/abs/2003.03600)
This seems like an interesting direction and it may require a specialized workflow. Ref: - [Derivative-Free Reinforcement Learning: A Review](https://arxiv.org/abs/2102.05710) - [BlackBoxOptim.jl](https://github.com/robertfeldt/BlackBoxOptim.jl) - [CMAEvolutionStrategy.jl](https://github.com/jbrea/CMAEvolutionStrategy.jl) - [BayesianOptimization.jl](https://github.com/jbrea/BayesianOptimization.jl) - [Evolutionary.jl](https://github.com/wildart/Evolutionary.jl) - [Evolving...
https://github.com/JuliaReinforcementLearning/ReinforcementLearningCore.jl/blob/63f306d99a6db736a1755a5d1e26f2aa8e8822dc/src/extensions/Zygote.jl#L10 Or maybe make a PR in Flux instead?
In current design of distributed rl, each worker creates an independent model and make predictions separately. A better solution might be that workers on the same node share some common...
See: https://arxiv.org/abs/1602.01783 . It described a RL method without replay memory. such as n-step Q-learning, A3C.
R2D2
Implementing the recurrent and distributed Rl algorithm R2D2(https://openreview.net/pdf?id=r1lyTjAqYX).
As said here https://github.com/JuliaReinforcementLearning/ReinforcementLearningZoo.jl/pull/93#issuecomment-699647922, id like to write down some thoughts regarding the network handling in this framework. Maybe this is also relevant to https://github.com/JuliaReinforcementLearning/ReinforcementLearningCore.jl. 1. I would like to...
We used to have support for Knet.jl in addition to Flux.jl, but it was dropped since [email protected]. The main reason was that Knet.jl is not very easy to extend. However,...
ref: https://arxiv.org/abs/1911.02140 Based on the implementation of IQN, this is relatively easy to support.
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A reinforcement learning package for Julia