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monte-carlo-pg-in-julia's Introduction

Monte Carlo Policy Gradient using Flux.jl

This is an implementation of the Monte Carlo Policy Gradient method, also known as REINFORCE method, in Julia using the Flux ML package. The algorithm has been tested on the CartPole-v0 environment.

Results

Rewards over episodes

rewards plot


Initial untrained policy

rewards plot

Final trained policy

rewards plot

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monte-carlo-pg-in-julia's Issues

Cannot reproduce the result

Hi, I've downloaded the code and try to run it. But I cannot get the same result as yours, it actually never converges. To make the code working, I only commented out below code in REINFORCE.jl, because Flux.Tracker is not supported anymore and CircularBuffer seems not used. Other than that nothing is changed.

using Flux.Tracker
using DataStructures: CircularBuffer

The output:

...
Episode 4987 = 21.0 | Mean of the last 10 episodes = 22.9
Episode 4988 = 24.0 | Mean of the last 10 episodes = 23.5
Episode 4989 = 40.0 | Mean of the last 10 episodes = 26.3
Episode 4990 = 18.0 | Mean of the last 10 episodes = 27.2
Episode 4991 = 26.0 | Mean of the last 10 episodes = 27.5
Episode 4992 = 37.0 | Mean of the last 10 episodes = 28.6
Episode 4993 = 20.0 | Mean of the last 10 episodes = 27.4
Episode 4994 = 25.0 | Mean of the last 10 episodes = 28.6
Episode 4995 = 27.0 | Mean of the last 10 episodes = 26.5
Episode 4996 = 33.0 | Mean of the last 10 episodes = 27.1
Episode 4997 = 12.0 | Mean of the last 10 episodes = 26.2
Episode 4998 = 31.0 | Mean of the last 10 episodes = 26.9
Episode 4999 = 13.0 | Mean of the last 10 episodes = 24.2
Episode 5000 = 15.0 | Mean of the last 10 episodes = 23.9

My environment is:

julia> versioninfo()
Julia Version 1.4.1
Commit 381693d3df* (2020-04-14 17:20 UTC)
Platform Info:
  OS: macOS (x86_64-apple-darwin18.7.0)
  CPU: Intel(R) Core(TM) i7-7567U CPU @ 3.50GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-8.0.1 (ORCJIT, skylake)
Environment:
  JULIA_NUM_THREADS = 2

(@v1.4) pkg> st
Status `~/.julia/environments/v1.4/Project.toml`
  [c52e3926] Atom v0.12.11
  [336ed68f] CSV v0.6.2
  [a93c6f00] DataFrames v0.21.0
  [31c24e10] Distributions v0.23.2
  [587475ba] Flux v0.10.4
  [56b9baea] Gym v0.1.0 #master (https://github.com/FluxML/Gym.jl)
  [e5e0dc1b] Juno v0.8.2
  [7f8f8fb0] LearnBase v0.4.1
  [91a5bcdd] Plots v1.3.1
  [438e738f] PyCall v1.91.4
  [0376cc21] Reinforce v0.3.0
  [2913bbd2] StatsBase v0.33.0
  [4c63d2b9] StatsFuns v0.9.4
  [9a3f8284] Random 
  [10745b16] Statistics

Any ideas?

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