This TD introduces different algorithms, frameworks and tools used in Reinforcement Learning. The methods are applied to the robotic field: a Cartpole and the PandaReachJointsDense environment.
## REINFORCE
The REINFORCE algorithm is used to solve the Cartpole environment. The plot showing the total reward accross episodes can be seen below: 
The python script used is: reinforce_cartpole.py.
## Familiarization with a complete RL pipeline: Application to training a robotic arm
### Stable-Baselines3 and HuggingFace
In this section, the Stable-Baselines3 package is used to solve the Cartpole with the Advantage Actor-Critic (A2C) algorithm.
The python code used is: a2c_sb3_cartpole.py.
The python code used is: ```a2c_sb3_cartpole.py```.
The trained model is shared on HuggingFace, available on the following link: https://huggingface.co/oscarchaufour/a2c-CartPole-v1
### Weights & Biases
The Weights & Biases package is used to visualize the taining and the performances of a model. The link to the run visualization on WandB is: https://wandb.ai/oscar-chaufour/a2c-cartpole-v1?workspace=user-oscar-chaufour
The evolution of certain metrics during the training can be visualized. For example the policy loss for at each step can be seen below: 
The evolution of certain metrics during the training can be visualized. For example the policy loss at each step can be seen below: 
The policy loss follows a decreasing trend, which is coherent to the model learning during the training phase.
### Full workflow with panda-gym
The full training-visualization-sharing workflow is applied to the PandaReachJointsDense environment. It appears that the PandaReachJointsDense-v2 environment is not known and could not be used (NameNotFound: Environment PandaReachJointsDense doesn't exist.)
## Contribute
This tutorial may contain errors, inaccuracies, typos or areas for improvement. Feel free to contribute to its improvement by opening an issue.
