Conservative q-learning for offline reinforcement learning
Effectively leveraging large, previously collected datasets in reinforcement learn-ing (RL) is
a key challenge for large-scale real-world applications. Offline RL algorithms promise to …
a key challenge for large-scale real-world applications. Offline RL algorithms promise to …
Social nce: Contrastive learning of socially-aware motion representations
Learning socially-aware motion representations is at the core of recent advances in multi-
agent problems, such as human motion forecasting and robot navigation in crowds. Despite …
agent problems, such as human motion forecasting and robot navigation in crowds. Despite …
Disagreement-regularized imitation learning
We present a simple and effective algorithm designed to address the covariate shift problem
in imitation learning. It operates by training an ensemble of policies on the expert …
in imitation learning. It operates by training an ensemble of policies on the expert …
Toward the fundamental limits of imitation learning
Imitation learning (IL) aims to mimic the behavior of an expert policy in a sequential decision-
making problem given only demonstrations. In this paper, we focus on understanding the …
making problem given only demonstrations. In this paper, we focus on understanding the …
On the value of interaction and function approximation in imitation learning
We study the statistical guarantees for the Imitation Learning (IL) problem in episodic MDPs.
Rajaraman et al.(2020) show an information theoretic lower bound that in the worst case, a …
Rajaraman et al.(2020) show an information theoretic lower bound that in the worst case, a …
Robust imitation of a few demonstrations with a backwards model
Behavior cloning of expert demonstrations can speed up learning optimal policies in a more
sample-efficient way over reinforcement learning. However, the policy cannot extrapolate …
sample-efficient way over reinforcement learning. However, the policy cannot extrapolate …
[PDF][PDF] Robust Multi-Domain Multi-Turn Dialogue Policy via Student-Teacher Offline Reinforcement Learning
M Rohmatillah, JT Chien - APSIPA Transactions on Signal …, 2024 - nowpublishers.com
Dialogue policy plays a crucial role in a dialogue system as it determines the system
response given a user input. In a pipeline system, the dialogue policy is susceptible to the …
response given a user input. In a pipeline system, the dialogue policy is susceptible to the …
Doubly constrained offline reinforcement learning for learning path recommendation
Y Yun, H Dai, R An, Y Zhang, X Shang - Knowledge-Based Systems, 2024 - Elsevier
Learning path recommendation refers to the application of interactive recommendation
systems in the field of education, aimed at optimizing learning outcomes while minimizing …
systems in the field of education, aimed at optimizing learning outcomes while minimizing …
Provably breaking the quadratic error compounding barrier in imitation learning, optimally
We study the statistical limits of Imitation Learning (IL) in episodic Markov Decision
Processes (MDPs) with a state space $\mathcal {S} $. We focus on the known-transition …
Processes (MDPs) with a state space $\mathcal {S} $. We focus on the known-transition …
HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach
Offline reinforcement learning (ORL) has gained attention as a means of training
reinforcement learning models using pre-collected static data. To address the issue of …
reinforcement learning models using pre-collected static data. To address the issue of …