Sharing information on extended reachability goals over propositionally constrained multi-agent state spaces

By exchanging propositional information, agents can implicitly reduce large domain state spaces, a feature that is particularly attractive for Reinforcement Learning approaches. This paper proposes a learning technique that combines a Reinforcement Learning algorithm and a planner for propositionally constrained state spaces, that autonomously help agents to implicitly reduce the state space towards possible plans that lead to a goal whilst avoiding irrelevant or inadequate states. State space constraints are communicated among the agents using a common constraint set based on extended reachability goals. A performance evaluation against standard Reinforcement Learning techniques showed that by extending autonomous learning with propositional constraints updated along the learning process can produce faster convergence to optimal policies due to early state space reduction caused by shared information on state space constraints.