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Found 6 papers, 5 papers with code
Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving
Especially in real-world application domains, such as autonomous driving, auto-curriculum generation is considered vital for obtaining robust and general policies.
Learning Adaptive Safety for Multi-Agent Systems
Ensuring safety in dynamic multi-agent systems is challenging due to limited information about the other agents.
Enhancing Robot Learning through Learned Human-Attention Feature Maps
In this paper, we propose a novel approach to model and emulate the human attention with an approximate prediction model.
Safe Policy Improvement in Constrained Markov Decision Processes
The automatic synthesis of a policy through reinforcement learning (RL) from a given set of formal requirements depends on the construction of a reward signal and consists of the iterative application of many policy-improvement steps.
Hierarchical Potential-based Reward Shaping from Task Specifications
The automatic synthesis of policies for robotic-control tasks through reinforcement learning relies on a reward signal that simultaneously captures many possibly conflicting requirements.
Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing
World models learn behaviors in a latent imagination space to enhance the sample-efficiency of deep reinforcement learning (RL) algorithms.
