Implements the full-state plain Q-learning algorithm A = FULLSTATE_PLAINQ(A, STATE, ACTION, REWARD, PARAMS) Implements full-state version of the plain Q-learning algorithm, employing in addition to that an eligibility trace. Uses flat Q- and eligibility tables for fast access. Supports discrete states and actions, with 1 action variable per agent. Required values on the agent learning parameters: alpha - the learning rate gamma - the discount factor lambda - the eligibility trace decay rate epsilon - the exploration probability Required values on the extra parameters: newtrial - only in episodic environments; whether a new trial is beginning Can be coupled with an action function that uses a Q-table indexed on full world state and agent action, such as fullstate_greedyact(). See also agent_learn, plainq, fullstate_plainq_init