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A new soft computing method for integration of expert's knowledge in reinforcement learn-ing problems
This paper proposes a novel fuzzy action selection method to leverage human
knowledge in reinforcement learning problems. Based on the estimates of the
most current action-state values, the proposed fuzzy nonlinear mapping as-signs
each member of the action set to its probability of being chosen in the next
step. A user tunable parameter is introduced to control the action selection
policy, which determines the agent's greedy behavior throughout the learning
process. This parameter resembles the role of the temperature parameter in the
softmax action selection policy, but its tuning process can be more
knowledge-oriented since this parameter reflects the human knowledge into the
learning agent by making modifications in the fuzzy rule base. Simulation
results indicate that including fuzzy logic within the reinforcement learning
in the proposed manner improves the learning algorithm's convergence rate, and
provides superior performance