• object oriented: all of the rl agents using the same framework(base class Agent), makes it easy to read and understand
• perfect reproduction: training result would be exactly the same under the same random seed

• DQN Playing Atari with Deep Reinforcement Learning, Human-level control through deep reinforcement learning
• DDQN Deep Reinforcement Learning with Double Q-learning
• Dueling DQN Dueling Network Architectures for Deep Reinforcement Learning
• DDQN with prioritized experience replay Prioritized Experience Replay
• REINFORCE(Monte-Carlo Policy Gradient, Vanilla Policy Gradient)
• REINFORCE with BASELINE
• DDPG Continuous control with deep reinforcement learning
• TD3 Addressing Function Approximation Error in Actor-Critic Methods
• SAC Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor , Soft Actor-Critic Algorithms and Applications
• PPO Proximal Policy Optimization Algorithms
• A3C Asynchronous Methods for Deep Reinforcement Learning

training result of the agent trying to solve a problem from a scratch

the original environment is hard to converge,
so I modify the reward to solve this problem and get the result below

Note that there is no goal for Pendulum-v0, but as you can see in the result, the agent did learn something

online training is not always stable
sometimes the agent gets a high reward(or running reward)
then its performance would decline rapidly.
so I choose some policy during the training to test the agent's performance

though training on a modified environment,
I still use the original one to test the policy,
thus illustrate the result the learning

• Deep Reinforcement Learning Algorithms with PyTorch By p-christ
• reinforcement learning examples of pytorch
• Author's PyTorch implementation of TD3
• RLkit
• Spinning-Up
• pytorch-A3C by MorvanZhou