Recursive Backwards Q-Learning in Deterministic Environments
This addresses a specific bottleneck in reinforcement learning for deterministic problems, offering a more efficient method for tasks like maze navigation.
The paper tackled the inefficiency of Q-learning in deterministic environments by introducing the recursive backwards Q-learning (RBQL) agent, which builds a model and propagates values backwards to achieve optimal solutions faster, as demonstrated by greatly outperforming regular Q-learning in maze pathfinding.
Reinforcement learning is a popular method of finding optimal solutions to complex problems. Algorithms like Q-learning excel at learning to solve stochastic problems without a model of their environment. However, they take longer to solve deterministic problems than is necessary. Q-learning can be improved to better solve deterministic problems by introducing such a model-based approach. This paper introduces the recursive backwards Q-learning (RBQL) agent, which explores and builds a model of the environment. After reaching a terminal state, it recursively propagates its value backwards through this model. This lets each state be evaluated to its optimal value without a lengthy learning process. In the example of finding the shortest path through a maze, this agent greatly outperforms a regular Q-learning agent.