Exploring the possibilities of reinforcement learning: An exciting AI final-year project

 Reinforcement learning is an exciting and rapidly growing field of Artificial Intelligence (AI). It involves the use of algorithms to teach machines how to make decisions in complex environments. This makes it a great choice for the artificial intelligence final-year project, as it allows students to explore the possibilities and potential applications of this powerful technology. 

At its core, reinforcement learning works by rewarding successful actions or behaviours with rewards or punishments. As such, reinforcement learning can be used for many different tasks from robotics navigation to game-playing AI agents. By trial-and-error methods, these agents learn from their mistakes and gradually become better at achieving their goals over time without any explicit programming instructions about what those goals should be or how they should achieve them. 

In order for an agent to effectively learn through reinforcement techniques however there must first exist some sort of reward system that encourages desired behaviour while punishing undesired behaviour - this is known as the "reward function". Once such reward functions have been established then various algorithms can be used in order to create effective policies which allow machines/agents to maximize rewards while minimizing risks associated with taking certain actions within a given environment - henceforth referred to as “exploration” strategies. 

 An interesting exploration strategy would involve using deep Q networks (DQNs) which are neural networks trained on large datasets comprising past experiences that enable them to identify patterns between states & corresponding action outcomes; thereby allowing intelligent decision-making based on expected future outcomes rather than relying solely on preprogrammed rulesets. Such models are very useful when dealing with problems where traditional rule-based systems may not work due to complexity & lack of clarity around state transitions e..g robotic control, autonomous vehicles etc  

 Another avenue worth exploring could include incorporating evolutionary computing into RL whereby genetic algorithm techniques are employed in combination DQNs so that best-performing policies evolve over time by selectively breeding more optimal ones thus enabling faster convergence towards desired objectives.  

 Overall, exploring possible application scenarios involving Reinforcement Learning presents immense opportunities both academically & commercially; making it one most fascinating topics to pursue during your final year project!