Value-Difference Based Exploration: Adaptive Control between Epsilon-Greedy and Softmax [chapter]

Michel Tokic, Günther Palm
2011 Lecture Notes in Computer Science  
This paper proposes "Value-Difference Based Exploration combined with Softmax action selection" (VDBE-Softmax) as an adaptive exploration/exploitation policy for temporal-difference learning. The advantage of the proposed approach is that exploration actions are only selected in situations when the knowledge about the environment is uncertain, which is indicated by fluctuating values during learning. The method is evaluated in experiments having deterministic rewards and a mixture of both
more » ... inistic and stochastic rewards. The results show that a VDBE-Softmax policy can outperform ε-greedy, Softmax and VDBE policies in combination with on-and off-policy learning algorithms such as Q-learning and Sarsa. Furthermore, it is also shown that VDBE-Softmax is more reliable in case of value-function oscillations.
doi:10.1007/978-3-642-24455-1_33 fatcat:eod4k25jnrdhrfix4ea67krjba