Reinforcement learning is a goal-oriented machine learning algorithm that trains an agent to make a sequence of decisions. In the case of deep learning models, we train them on existing data and apply the learning on new or unseen data. Reinforcement learning exhibits dynamic learning by adjusting its own actions based on continuous feedback in order to maximize the reward. We can introduce deep learning into a reinforcement learning system, which is known as deep reinforcement learning.
RL4J is a reinforcement learning framework integrated with DL4J. RL4J supports two reinforcement algorithms: deep Q-learning and A3C (short for Asynchronous Actor-Critic Agents). Q-learning is an off-policy reinforcement learning algorithm that seeks the best action for the given state. It learns from actions outside the ones mentioned in the current policy...
