What modifications can maximize the efficacy of the REINFORCE algorithm for a policy gradient task?

• examples

  23 21,727 7.7 Python

  A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

  

I am straying out of my domain knowledge to attempt a basic reinforcement learning task in a toy environment and have become fairly familiar with the REINFORCE algorithm for policy gradient agents, especially PyTorch’s implementation (found here). It is clear to me now that there are superior methods to train RL agents (PPO for instance), but as I read, these feel beyond my current intellectual or time resources. As such, I’d like to eek out as much power through modifications of REINFORCE as possible before determining how I might move on.

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