Two Novel On-policy Reinforcement Learning Algorithms based on TD(λ)-methods

This paper describes two novel on-policy reinforcement learning algorithms, named QV(λ)-learning and the actor critic learning automaton (ACLA). Both algorithms learn a state value-function using TD(λ)-methods. The difference between the algorithms is that QV-learning uses the learned value function and a form of Q-learning to learn Q-values, whereas ACLA uses the value function and a learning automaton-like update rule to update the actor. We describe several possible advantages of these methods compared to other value-function-based reinforcement learning algorithms such as Q-learning, Sarsa, and conventional actor-critic methods. Experiments are performed on (1) small, (2) large, (3) partially observable, and (4) dynamic maze problems with tabular and neural network value-function representations, and on the mountain car problem. The overall results show that the two novel algorithms can outperform previously known reinforcement learning algorithms