Reinforcement Learning – Reward for Learning
Reinforcement learning can be understood by using the concepts of agents, environments, states, actions and rewards. This is an area of machine learning; where there’s no answer key, but RL agent still has to decide how to act to perform its task. The agent is inspired by behaviourist psychology who decide how and what actions will taken in an environment to maximize some notion of cumulative reward.
The post Reinforcement Learning – Reward for Learning appeared first on Vinod Sharma’s Blog.
Already a leader in the advancement of artificial intelligence, IBM has brought AI technology to developers with open arms. IBM recently launched a series of projects for developers to access open source code and services to build AI and machine learning applications. The vendor wants to democratize these technologies, so they can be easily accessed […]