Reinforcement field

Complex control tasks involving varying or evolving system dynamics often pose a great challenge to mainstream reinforcement learning algorithms. Specifically, in most standard methods, actions are often assumed to be a concrete and fixed set that applies to the state space in a predefined manner. Consequently, without resorting to a substantial re-learning procedure, the derived policy lacks the ability to adapt to variations in action outcomes or shifts in the action set. In addition, the standard action representation and its attendant state transition mechanism limit the applicability of the RL framework in complex domains primarily due to the intractability of the resulting large state space and lack of the facility to generalize the learned policy to the unknown parts of the state space. This paper proposes an alternative view of reinforcement learning by establishing the notion of the reinforcement field through a collection of policy-embedded particles gathered during the policy learning process. The reinforcement field serves as a policy generalization mechanism through the use of kernel functions as a state correlation hypothesis in combination with Gaussian process regression as a value function approximator.