Reinforcement-Learning

In Robotics, one of the hardest problems is how to make your model transfer to the real world. Due to the sample inefficiency of deep RL algorithms and the cost of data collection on real robots, we often need to train models in a simulator which theoretically provides an infinite amount of data. However, the reality gap between the simulator and the physical world often leads to failure when working with physical robots. The gap is triggered by an inconsistency between physical parameters (i.e. friction, kp, damping, mass, density) and, more fatally, the incorrect physical modeling (i.e. collision between soft surfaces). ...

The full implementation is available in lilianweng/deep-reinforcement-learning-gym In the previous two posts, I have introduced the algorithms of many deep reinforcement learning models. Now it is the time to get our hands dirty and practice how to implement the models in the wild. The implementation is gonna be built in Tensorflow and OpenAI gym environment. The full version of the code in this tutorial is available in [lilian/deep-reinforcement-learning-gym]. ...

[Updated on 2018-06-30: add two new policy gradient methods, SAC and D4PG.] [Updated on 2018-09-30: add a new policy gradient method, TD3.] [Updated on 2019-02-09: add SAC with automatically adjusted temperature]. [Updated on 2019-06-26: Thanks to Chanseok, we have a version of this post in Korean]. [Updated on 2019-09-12: add a new policy gradient method SVPG.] [Updated on 2019-12-22: add a new policy gradient method IMPALA.] [Updated on 2020-10-15: add a new policy gradient method PPG & some new discussion in PPO.] [Updated on 2021-09-19: Thanks to Wenhao & 爱吃猫的鱼, we have this post in Chinese1 & Chinese2]. ...

[Updated on 2020-09-03: Updated the algorithm of SARSA and Q-learning so that the difference is more pronounced. [Updated on 2021-09-19: Thanks to 爱吃猫的鱼, we have this post in Chinese]. ...

The algorithms are implemented for Bernoulli bandit in lilianweng/multi-armed-bandit. Exploitation vs Exploration The exploration vs exploitation dilemma exists in many aspects of our life. Say, your favorite restaurant is right around the corner. If you go there every day, you would be confident of what you will get, but miss the chances of discovering an even better option. If you try new places all the time, very likely you are gonna have to eat unpleasant food from time to time. Similarly, online advisors try to balance between the known most attractive ads and the new ads that might be even more successful. ...