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Reinforcement Learning (RL) methods are widely used for dynamic control tasks. In many cases, these are high risk tasks where the trial and error process may select actions which execution from unsafe states can be catastrophic. In addition, many of these tasks have continuous state and action spaces, making the learning problem harder and unapproachable with conventional RL algorithms. So, when the agent begins to interact with a risky and large state-action space environment, an important question arises: how can we avoid that the exploration of the state-action space causes damages in the learning (or other) systems. In this paper, we define the concept of risk and address the problem of safe exploration in the context of RL. Our notion of safety is concerned with states that can lead to damage. Moreover, we introduce an algorithm that safely improves suboptimal but robust behaviors for continuous state and action control tasks, and that learns efficiently from the experience gathered from the environment. We report experimental results using the helicopter hovering task from the RL Competition.