A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments

• URL: http://arxiv.org/abs/2408.00997v1
• Date: Fri, 2 Aug 2024 04:09:30 GMT
• Title: A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments
• Authors: Erfan Entezami, Mahsa Sahebdel, Dhawal Gupta,
• Abstract summary: In safety-constrained environments, utilizing unsupervised exploration or a non-optimal policy may lead the agent to undesirable states. We introduce a new exploration framework for navigating the grid environments that enables model-free agents to interact with the environment while adhering to safety constraints.
• Score: 2.5037136114892267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training a model-free reinforcement learning agent requires allowing the agent to sufficiently explore the environment to search for an optimal policy. In safety-constrained environments, utilizing unsupervised exploration or a non-optimal policy may lead the agent to undesirable states, resulting in outcomes that are potentially costly or hazardous for both the agent and the environment. In this paper, we introduce a new exploration framework for navigating the grid environments that enables model-free agents to interact with the environment while adhering to safety constraints. Our framework includes a pre-training phase, during which the agent learns to identify potentially unsafe states based on both observable features and specified safety constraints in the environment. Subsequently, a binary classification model is trained to predict those unsafe states in new environments that exhibit similar dynamics. This trained classifier empowers model-free agents to determine situations in which employing random exploration or a suboptimal policy may pose safety risks, in which case our framework prompts the agent to follow a predefined safe policy to mitigate the potential for hazardous consequences. We evaluated our framework on three randomly generated grid environments and demonstrated how model-free agents can safely adapt to new tasks and learn optimal policies for new environments. Our results indicate that by defining an appropriate safe policy and utilizing a well-trained model to detect unsafe states, our framework enables a model-free agent to adapt to new tasks and environments with significantly fewer safety violations.

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