CEM: Constrained Entropy Maximization for Task-Agnostic Safe Exploration

Without an assigned task, a suitable intrinsic objective for an agent is to explore the environment efficiently. However, the pursuit of exploration will inevitably bring more safety risks.<br/>An under-explored aspect of reinforcement learning is how to achieve safe efficient exploration when the task is unknown.<br/>In this paper, we propose a...

Reinforcement Learning by Guided Safe Exploration

Safety is critical to broadening the application of reinforcement learning (RL). Often, we train RL agents in a controlled environment, such as a laboratory, before deploying them in the real world. However, the real-world target task might be unknown prior to deployment. Reward-free RL trains an agent without the reward to adapt quickly once...

Training and Transferring Safe Policies in Reinforcement Learning

Safety is critical to broadening the a lication of reinforcement learning (RL). Often, RL agents are trained in a controlled environment, such as a laboratory, before being de loyed in the real world. However, the target reward might be unknown rior to de loyment. Reward-free RL addresses this roblem by training an agent without the reward to...

Refined Risk Management in Safe Reinforcement Learning with a Distributional Safety Critic

Safety is critical to broadening the real-world use of reinforcement learning (RL). Modeling the safety aspects using a safety-cost signal separate from the reward is becoming standard practice, since it avoids the problem of finding a good balance between safety and performance. However, the total safety-cost distribution of different...

A Modern Perspective on Safe Automated Driving for Different Traffic Dynamics using Constrained Reinforcement Learning

The use of reinforcement learning (RL) in real-world domains often requires extensive effort to ensure safe behavior. While this compromises the autonomy of the system, it might still be too risky to allow a learning agent to freely explore its environment. These strict impositions come at the cost of flexibility and applying them often relies...

WCSAC: Worst-Case Soft Actor Critic for Safety-Constrained Reinforcement Learning

Safe exploration is regarded as a key priority area for reinforcement learning research. With separate reward and safety signals, it is natural to cast it as constrained reinforcement learning, where expected long-term costs of policies are constrained. However, it can be hazardous to set constraints on the expected safety signal without...