Directional Constraints for Efficient Exploration in Safe Reinforcement Learning
Reinforcement Learning has revolutionized the landscape of robotic research, allowing robust learning of complex robotic skills in simulation. However, real-world deployment in open-ended environments requires strong safety guarantees to prevent dangerous or harmful behaviors. Safe Reinforcement Learning methods address this requirement by enforcing safety constraints. Nevertheless, learning under constraints often reduces learning speed and could lead to suboptimal task performance, as the agent must solve a more complex constrained optimization problem compared to unconstrained settings. To
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- PossiblePossibly related (embedding) · 50%Gradient-based Planning for World Models at Longer Horizons →
- PossiblePossibly related (embedding) · 48%Unity-Technologies/ml-agents →
- PossiblePossibly related (embedding) · 48%AgentCore-8B →
- PossiblePossibly related (embedding) · 47%Autonomous navigation of intelligent microrobotic swarms in unknown environments →
- PossiblePossibly related (embedding) · 46%hscspring/rl-llm-nlp →
- LinkedLinked via arxiv author · 85%Paolo Magliano →
“Directional Constraints for Efficient Exploration in Safe Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Puze Liu →
“Directional Constraints for Efficient Exploration in Safe Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Jan Peters →
“Directional Constraints for Efficient Exploration in Safe Reinforcement Learning”