We propose a reputation oriented reinforcement learning algorithm for buying agents in electronic market environments. We take into account the fact the quality of a good offered by different selling agents may not be the same, and a selling agent may alter the quality of its goods. In our approach, buying agents learn to avoid the risk of purchasing low quality goods and to maximize their expected value of goods by dynamically maintaining sets of reputable and disreputable sellers. Modelling the reputation of sellers allows buying agents to focus on those sellers with whom a certain degree of trust has been established. We also include the ability for buying agents to explore the marketplace in order to discover new reputable sellers. In this paper, we focus on presenting the experimental results that confirm the improved satisfaction for buying agents that model reputation according to our algorithm.