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Found 7 papers, 2 papers with code
A distributional view on multi objective policy optimization
Many real-world problems require trading off multiple competing objectives.
Imitating Language via Scalable Inverse Reinforcement Learning
We focus on investigating the inverse reinforcement learning (IRL) perspective to imitation, extracting rewards and directly optimizing sequences instead of individual token likelihoods and evaluate its benefits for fine-tuning large language models.
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning
We apply multi-agent deep reinforcement learning (RL) to train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision.
Replay across Experiments: A Natural Extension of Off-Policy RL
Replaying data is a principal mechanism underlying the stability and data efficiency of off-policy reinforcement learning (RL).
Explicit Pareto Front Optimization for Constrained Reinforcement Learning
We propose a framework that uses a multi-objective RL algorithm to find a Pareto front of policies that trades off between the reward and constraint(s), and simultaneously searches along this front for constraint-satisfying policies.
Exploring Exploration: Comparing Children with RL Agents in Unified Environments
Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn.
Adversarial Attacks on Neural Network Policies
Machine learning classifiers are known to be vulnerable to inputs maliciously constructed by adversaries to force misclassification.
