Implements deterministic greedy action selection for adaptive state focus Q-learning [A, ACTION] = ASFQ_GREEDYACT(A, STATE, ACTIONS, REWARDS, PARAMS) Implements deterministic greedy action selection, to be combined with the adaptive state focus Q-learning algorithm. The action with the highest Q-value is chosen (breaking ties randomly). The action selection mechanism takes into account the learning mode of the agent (single-agent, or full state), and thus the structure of the Q-table. Supports discrete states and actions, with 1 action variable only. Requires the size of the Q-table to be cached under a field named 'qsize'. See also agent_act, asfq