DRL.jl

Deep Reinforcement Learning for Julia

It's actually not so gradiose.

Julia implementations of deep reinforcement learning algorithms. Uses the POMDPs.jl framework for representing (Partially Observable) Markov Decision Proccesses (which itself is a framework for describing sequential decision making problems).

Working-ish:

• Deep Q Learning (DQN)

Currently working on:

• Model-Free Episodic Control (QEC)
• Deterministic Policy Gradient (DDPG)

To work on:

• Stochastic Policy Gradient (SDPG)
• Trust Region Policy Optimization (TRPO)
• Trust Region Generalized Advantage Estimate (TRGAE)
• VIME
• Misc NN models for MXNet (GAN, VAE, RNN)

NOTE: the signature for solve doesn't exactly match POMDPs.solve. It is `solve(::Solver, ::MDP, ::Policy, ::rng) NOTE: try to define your vectors as Float32 if possible (which is what mxnet uses)

Documentation someday