/Policy-Gradient-and-Reinforcement-Learning-Resources

Contains useful links for Policy Gradient and Reinforcement Learning

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Policy-Gradient-and-Reinforcement-Learning-Resources

Contains useful links for Policy Gradient and Reinforcement Learning

Deep RL bootcamp 2017 :- https://sites.google.com/view/deep-rl-bootcamp/lectures

Policy Gradient vs Value Based Methods :- https://www.freecodecamp.org/news/an-introduction-to-policy-gradients-with-cartpole-and-doom-495b5ef2207f/

Safety Gym (Safe RL) - https://openai.com/blog/safety-gym/

Step by step policy gradient - https://towardsdatascience.com/policy-gradient-step-by-step-ac34b629fd55

Machine Learning 2020 roadmap - https://whimsical.com/CA7f3ykvXpnJ9Az32vYXva

Policy based RL the easy way - https://towardsdatascience.com/policy-based-reinforcement-learning-the-easy-way-8de9a3356083

Tensorboard with Keras - https://www.machinecurve.com/index.php/2019/11/13/how-to-use-tensorboard-with-keras/

https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras

Carla leaderboard - https://leaderboard.carla.org/get_started/

https://leaderboard.carla.org/

Pytorch Tutorial - https://www.youtube.com/watch?v=DbeIqrwb_dE&list=PLqnslRFeH2UrcDBWF5mfPGpqQDSta6VK4&index=3

PPO for beginners :- https://medium.com/@eyyu/coding-ppo-from-scratch-with-pytorch-part-3-4-82081ea58146

A Really nice Policy Gradient Tutorial :- https://www.youtube.com/watch?v=cQfOQcpYRzE

Python Simulink Integration :- https://medium.com/@soutrikbandyopadhyay/controlling-a-simulink-model-by-a-python-controller-2b67bde744ee

Types of RL methods : https://smartlabai.medium.com/reinforcement-learning-algorithms-an-intuitive-overview-904e2dff5bbc

A blog on RL : https://towardsdatascience.com/crystal-clear-reinforcement-learning-7e6c1541365e

Problems with reward functions : https://towardsdatascience.com/how-learning-reward-functions-can-go-wrong-6e794e42f4fc

PPO Hacks : https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/

Pytorch Tutorials : https://www.youtube.com/playlist?list=PLhhyoLH6IjfxeoooqP9rhU3HJIAVAJ3Vz