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Found 8 papers, 3 papers with code
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning
Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset.
PAGAR: Taming Reward Misalignment in Inverse Reinforcement Learning-Based Imitation Learning with Protagonist Antagonist Guided Adversarial Reward
Many imitation learning (IL) algorithms employ inverse reinforcement learning (IRL) to infer the intrinsic reward function that an expert is implicitly optimizing for based on their demonstrated behaviors.
POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems
We also present a novel approach to propagate TMs more efficiently and precisely across ReLU activation functions.
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines
A misspecified reward can degrade sample efficiency and induce undesired behaviors in reinforcement learning (RL) problems.
Programmatic Reward Design by Example
In this paper, we propose the idea of programmatic reward design, i. e. using programs to specify the reward functions in RL environments.
POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems
We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs).
Runtime-Safety-Guided Policy Repair
The key idea behind our approach is the formulation of a trajectory optimization problem that allows the joint reasoning of policy update and safety constraints.
Safety-Aware Apprenticeship Learning
Apprenticeship learning (AL) is a kind of Learning from Demonstration techniques where the reward function of a Markov Decision Process (MDP) is unknown to the learning agent and the agent has to derive a good policy by observing an expert's demonstrations.
