Reinforcement Learning
- 1 min In reinforcement learning, the model (called agent) interacts with its environment by choosing from a set of possible actions (action space) in each state of the environment that cause either positive or negative rewards from the environment. Think of rewards as an abstract concept of signalizing that the action taken was good or bad. Thereby, the reward issued by the environment can be immediate or delayed into the future. By learning from the combination of environment states, actions and corresponding rewards (so called transitions), the agent tries to reach an optimal set of decision rules (the policy) that maximize the total reward gathered by the agent in each state.
In the following project the attempt was made to compare different Reinforcement Learning strategies in order to play a game. In more detail a Q-Network was compered to a policy network for Mario Brose Game