Implement Reinforcement Learning End-to-End
Train an agent to explore an environment using Reinforcement learning and then use the optimized policy generated to perform the task like a pro.
If you’re wondering how to apply reinforcement learning (RL) to play a game like a champ, read on…
To accomplish the goal, you need to train an RL agent that uses trial and error to explore the game(environment) by interacting with the game and finding what actions in different states will yield the maximum reward in the long run.
Once the Agent has learned an optimized policy, an optimized policy is a decision-making strategy used to make decisions in a given environment. An RL agent now knows the best decisions to take in a given environment to maximize the expected cumulative reward over time, allowing you to play the game like a pro.
The end-to-end process for an agent to train and apply RL in any environment can be broken down into several steps:
- Define the goal
- Initialize the environment
- Interact with the environment
- Find an optimal policy
- Apply the optimal policy
Define the goal
The first step in applying RL to a problem is defining the Agent's goal and understanding the environment in which it operates. This includes identifying…
