Offline reinforcement learning (RL) is an area of study that has been re-emerging. It is also called batch reinforcement learning or batch RL. Offline reinforcement learning has one sole objective - learning the behaviors of machine learning models using only the data logged beforehand. Here we refer to the data from previous experiments or historical datasets that human demonstrations have collected. This data collection has been done without interacting with the environment beyond the datasets.
How is Offline Reinforcement Learning helpful?
Offline RL is a feasible path that can be worked around. It advances the ML model in the direction of valuable real-world RL. Offline RL only needs machine learning skills that depend entirely on the datasets that have been previously collected. The models do not need any interaction with the complete data environment. This way, the historical datasets are utilized effectively. These datasets have been collected from various sources that include -
- Previous experiments
- Domain-specific solutions
- Relevant data problems
- Human demonstrations
These data sources help in building complex engines for making multiple decisions from a single data source.
In reinforcement learning (RL), a statistical agent interacts with the data environment by following a procedure or course of action defined by the new state and the reward for this transition. The ultimate objective of Reinforcement Learning is to develop the best policy which increases the long-standing cumulative results.
Applications of Offline Reinforcement Learning
Offline reinforcement learning, or offline RL, has a high potential for remarkable development in many decision-making problems in the real world. It can reduce the prices of data collection. But which fields are we talking about? Some of them are given below –
- Discovery of new medicinal drugs
- Generation of dialogues through speech text audio generation
- Recommendation systems
- Driverless vehicles and autonomous driving
- Healthcare
- Education
- Robotics
Such examples and use cases assure finding solutions to critical global challenges like developing machines with a reduced carbon footprint. Therefore, offline RL has a lot of applications that can bring positive changes and revolutionize our growth in the future.
Why Reinforcement Learning Is Difficult For AI to Master?
Some problems exist in the whole concept of reinforcement learning that makes it difficult for AI to master. For example, it may be somewhat easy to measure what success is in an experiment. Let us take an example of something in the real world, such as the game of Go. If you look at all the cases of success, then it is not a straightforward concept. Therefore success concerning reinforcement learning not always is not something that can be obtained easily.
A logical machine like a computer also does not understand reward sparsity. That simply means that a computer cannot learn from its mistakes. This is because it does not know what action and result will bring it closer to success.
Imitation Learning was utilized to solve this problem for a better outcome in such real-world problems.
What Is Imitation Learning?
Imitation Learning is when an ML system observes an action, and then it tries to repeat it. This is an inherent and active or living concept in AI and ML. One more thing that needs to be kept in mind is that it is difficult to reproduce actions and observations in artificial intelligence.
Why Is Imitation Learning Required?
Several reinforcement learning algorithms use the received rewards approach to get the best outcome from a problem. Most of the time, these methods deliver good or at least optimal results. But in some cases, the learning process is quite challenging, especially in an environment with sparse rewards, like a game of chess where success is rewarded when you win or lose. There are some systems where rewards don’t exist at all, like a self-driving car. In those cases, Reinforcement Learning is not a successful model.
A practical solution for this real-world problem would be imitation learning or IL. Instead of using the reward system, the model or the agent tries to learn the most favorable procedure by following or imitating the experts' decisions.
Difference between Offline Reinforcement Learning and Imitation Learning
Before enlisting, the differences between offline RL and imitation learning can be explained with the help of a practical example of artificial intelligence.
AlphaGo is a computer that beats Lee Sedol at a game of Go, which is a strategy game. AlphaGo did not learn to play Go by copying the moves of other players. Instead, AlphaGo learned to play Go by playing thousands of times, repeating multiple moves and keeping track of the results of its games. It computed a strategy that worked well for a win and with which it lost games. The good results were then reinforced.
Differences
- Reinforcement Learning computes the best strategy by multiple input datasets for a successful outcome, whereas imitation learning uses the dataset of an expert.
- Reinforcement Learning has a complex way of working, whereas imitation learning follows a less cumbersome approach to get success.
- Imitation learning is dependent on offline learning.
- Reinforcement Learning has access to rewards, but imitation learning has no such access to any reward system.
- Imitation learning uses external datasets, whereas reinforcement learning makes its datasets and develops an optimal success policy from these datasets.
To sum up, this is an overview of offline RL and imitation learning and how they are different from each other. Also, you get to comprehend their significance and how AI models are associated with them. If you plan to implement these models, then E2E Networks has the right computing solutions for you.
Reference Links
https://deepai.org/machine-learning-glossary-and-terms/imitation-learning
https://dl.acm.org/doi/10.1145/3054912
https://smartlabai.medium.com/a-brief-overview-of-imitation-learning-8a8a75c44a9c
https://arxiv.org/abs/2005.01643
https://towardsdatascience.com/the-power-of-offline-reinforcement-learning-5e3d3942421c
https://offline-rl-neurips.github.io/2021/
https://bair.berkeley.edu/blog/2020/12/07/offline/
