RL algorithms can be used to solve tasks where automation is required. However actual implementation is easier said than done. You can ease your pain by using TF-Agents, a flexible library for TensorFlow to build reinforcement learning models. TF-Agents makes it easy to use reinforced learning for TensorFlow. TF-Agents enables newbies to learn RL using Colabs, documentation, and examples as well as researchers who want to build new RL algorithms.

Reinforcement learning has become a trending topic among all the tech giants and none of them is sitting back to catch up on this. OpenAI Baselines is a set of high-quality implementations of reinforcement learning algorithms. These algorithms make it easier for the research community to replicate, refine, and identify new ideas to create good baselines to build research on top of. They have beautifully abstracted the details of the RL algorithms and have made the use of these techniques as easy as calling a single class and feeding it essential details like environment name and batch sizes. This has made experimentation much easier and implementation simpler for the people new to the field.

Here is a short video of Orso's minimal path through his turf. Notice how he avoids troublesome water in favor of longer paths through grassy lands. In our Half Day Hands-on Training At ODSC West in San Francisco we will show you more details on how you can use reinforcement learning practically. Using Colab notebooks we will model problems as a simulation environment (Orso's world) and train your agent (Orso himself) to learn a good strategy.