A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges
Inverse Constrained Reinforcement Learning (ICRL) is the task of inferring the implicit
constraints followed by expert agents from their demonstration data. As an emerging …
constraints followed by expert agents from their demonstration data. As an emerging …
Oasis: Conditional distribution shaping for offline safe reinforcement learning
Offline safe reinforcement learning (RL) aims to train a policy that satisfies constraints using
a pre-collected dataset. Most current methods struggle with the mismatch between imperfect …
a pre-collected dataset. Most current methods struggle with the mismatch between imperfect …
Confidence aware inverse constrained reinforcement learning
SG Subramanian, G Liu, M Elmahgiubi… - arXiv preprint arXiv …, 2024 - arxiv.org
In coming up with solutions to real-world problems, humans implicitly adhere to constraints
that are too numerous and complex to be specified completely. However, reinforcement …
that are too numerous and complex to be specified completely. However, reinforcement …
Provably efficient exploration in inverse constrained reinforcement learning
To obtain the optimal constraints in complex environments, Inverse Constrained
Reinforcement Learning (ICRL) seeks to recover these constraints from expert …
Reinforcement Learning (ICRL) seeks to recover these constraints from expert …
TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles
While modern Autonomous Vehicle (AV) systems can develop reliable driving policies under
regular traffic conditions, they frequently struggle with safety-critical traffic scenarios. This …
regular traffic conditions, they frequently struggle with safety-critical traffic scenarios. This …
Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare
Reinforcement Learning (RL) applied in healthcare can lead to unsafe medical decisions
and treatment, such as excessive dosages or abrupt changes, often due to agents …
and treatment, such as excessive dosages or abrupt changes, often due to agents …
Safety through feedback in Constrained RL
In safety-critical RL settings, the inclusion of an additional cost function is often favoured
over the arduous task of modifying the reward function to ensure the agent's safe behaviour …
over the arduous task of modifying the reward function to ensure the agent's safe behaviour …
Learning Constraints from Offline Demonstrations via Superior Distribution Correction Estimation
G Quan, G Liu - Forty-first International Conference on Machine … - openreview.net
An effective approach for learning both safety constraints and control policies is Inverse
Constrained Reinforcement Learning (ICRL). Previous ICRL algorithms commonly employ …
Constrained Reinforcement Learning (ICRL). Previous ICRL algorithms commonly employ …