Safe Reinforcement Learning
"Safe reinforcement learning" refers to an extension of traditional reinforcement learning that focuses on ensuring the safety of the agent and its environment. In traditional reinforcement learning, an agent is trained to learn tasks in an environment by selecting actions and receiving rewards based on its actions. However, this can lead to risky or dangerous actions that could have undesirable consequences in the real world.
Safe reinforcement learning aims to avoid or minimize such undesirable consequences.
This can be achieved in a number of ways, including
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Exploration of safe actions: The agent is trained to be conservative in its actions and avoid risky actions. This can be achieved through the use of safety criteria or constraints.
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Simulation and testing: Safety-critical situations are tested in simulated environments or in an abstract way to ensure that the agent can act safely in the real environment.
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Human supervision: A human operator can be involved in the training process to prevent potentially dangerous actions and correct the agent.
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Evaluation functions for safety aspects: The agent can incorporate safety scores or critiques in addition to the normal reward functions to address safety-related issues.
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