In a self-organized Long Term Evolution (LTE) network, different Self-Organizing Network (SON) functions or different instances of the same SON function can execute parallel actions, which interact or collide among each other. When the effect of the interaction negatively affects the performances of the system, this is referred to in 3rd Generation Partnership Project (3GPP) as a SON conflict, which needs to be handled by means of a self-coordination framework. We focus on the self-coordination of different actions taken by two SON functions in a distributed SON (D-SON) architecture, which implements the SON functions at the edges of the network. We propose a multi-agent framework where each Enhanced Node Base station (eNB), is an autonomous agent modeled by means of a Markov Decision Process (MDP). We subdivide this global Markov Decision problem onto simpler subMDPs modeling the different SON functions. Each sub-problem is defined as an MDP and solved independently, and their individual policies are combined to obtain a global policy. This combined policy can execute several actions per state in parallel, but can introduce policy conflicts, which model the mentioned SON conflicts. Each subMDP is solved by means of a Reinforcement Learning (RL) approach. We focus on the SON conflict generated by the concurrent execution of Coverage and Capacity Optimization (CCO) and Inter-Cell Interference Coordination (ICIC) SON functions, which may require to update the same parameter, i.e. the transmission power level. Coordination among different actions is achieved by means of a coordination game where the players are the subMDPs and the actions and rewards are those provided by the independent RL solutions. Performance evaluation is carried out in a ns3 release 10 compliant LTE system simulator and it shows that our self-coordination approach provides satisfying solutions in terms of system performances for both the conflicting SON functions.