I am a researcher working on automation tasks using deep reinforcement learning. The paper and the reality were quite different, and there was a lot of difficulty during the automation task. I create this repository to help those who start a task using deep reinforcement learning. All of the code is in tensorflow and Python 3.

• The code in this repo has a simple and pretty structure in which each algorithm achieves uniformity. It will be a huge help for you to understand the differences between the reinforcement learning algorithms.
• Take advantage of the pseudo code folder. You should be able to see pseudo code and make all the algorithms into a consistent architecture.

We used open-gym cartpoles (for discrete tasks), mountain car (for continous tasks), and pendulum (for continous tasks). In the case of HER, we used the coin flipping environment cited in the paper.

• Policy Gradient (PG) for cartpole (discrete task)
• Advantage Actor-Critic (A2C) for cartpole (discrete task)
• Advantage Actor-Critic (A2C) for mountain car (continuous task),,,(imperfect...!!!)
• Proximal Policy Optimization (PPO) (continuous task)
• Deep Q Network (DQN) for cartpole (discrete task)
• Deep Deterministic Policy Gradient (DDPG) for pendulum (continuous task)
• Hindsight Experience Replay for coin flipping (discrete task),,,(imperfect...!!!)
• Soft Actor-Critic (SAC) for pendulum (continuous task)

• Policy Gradient (PG) [Paper] [Pseudo Code]
• Proximal Policy Optimization (PPO) [Paper] [Pseudo Code]
• Asynchronous Advantage Actor-Critic (A3C) [Paper] [Pseudo Code]
• Deep Q-learning Network (DQN) [Paper] [Pseudo Code]
• Deep Deterministic Policy Gradient (DDPG) [Paper] [Pseudo Code]
• Hindsight Experience Replay (HER) [Paper] [Pseudo Code]
• Soft Actor-Critic (SAC) [Paper] [Pseudo Code]

• PG (cartpole) vs A2C (cartpole)
• A2C (cartpole) vs A2C (pendulum)
• A2C (pendulum) vs PPO (pendulum)
• PG (cartpole) vs DQN (cartpole)
• A2C (pendulum) vs DDPG (pendulum)
• DQN (cartpole) vs HER (coin)
• PPO (pendulum) vs SAC (pendulum)

• A positive reward is a magnet, and a negative reward is a mole game.
• The fastest agent to learn the Pendulum task is DDPG. So is ddpg the best reinforcement learning algorithm?
• Which of the sparse reward and dense reward is practical?
• If the problem is difficult, split it up and approach it.
• Without a simulator, there is no answer other than model-based running.
• In very difficulut task, it does not affect the skill drawn from experience replay.
• Hindsight Experience Replay changes reward function. Unless you are a master of reinforcement learning, do not try HER.

pip install -r requirements.txt

• OpenAI - Spinning Up
• Reinforcement Learning Methods and Tutorials
• OpenAI - Baselines