Hands-on Reinforcement Learning

MOREAU Maxime, 3A - Computer science & M2 DS, ECL22

1. RL for CartPole-v1

1.1 Training

• Policy Network: One simple hidden layer fully connected + softmax

• Reinforcement: Policy Gradient

• Save: policy_cartpole.pth

• Code: reinforce_cartpole.py Below is the rewards accross 300 episodes :

1.2 Evaluation

• Code: evaluate_reinforce_cartpole.py The evaluation has been done one 100 episodes and the sucess threshold is set at a score of 400.

We finally have an evaluation with 100% of sucess:

2. Complete RL pipeline to solve CartPole environment with A2C.

Here we set up a complete pipeline to solve Cartpole environment with A2C algorithm.

Wandb has been set up to follow the learning phase.