Model-based Reinforcement Learning
195 papers with code • 0 benchmarks • 1 datasets
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POPGym
Latest papers with no code
Control in Stochastic Environment with Delays: A Model-based Reinforcement Learning Approach
In this paper we are introducing a new reinforcement learning method for control problems in environments with delayed feedback.
Scheduled Curiosity-Deep Dyna-Q: Efficient Exploration for Dialog Policy Learning
Therefore, we propose Scheduled Curiosity-Deep Dyna-Q (SC-DDQ), a curiosity-driven curriculum learning framework based on a state-of-the-art model-based reinforcement learning dialog model, Deep Dyna-Q (DDQ).
Locality Sensitive Sparse Encoding for Learning World Models Online
Unfortunately, NN-based models need re-training on all accumulated data at every interaction step to achieve FTL, which is computationally expensive for lifelong agents.
Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies
Causal emergence theory aims to bridge these two concepts and even employs measures of causality to quantify emergence.
Dynamic Programming-based Approximate Optimal Control for Model-Based Reinforcement Learning
This article proposes an improved trajectory optimization approach for stochastic optimal control of dynamical systems affected by measurement noise by combining optimal control with maximum likelihood techniques to improve the reduction of the cumulative cost-to-go.
Sample-Efficient Learning to Solve a Real-World Labyrinth Game Using Data-Augmented Model-Based Reinforcement Learning
Motivated by the challenge of achieving rapid learning in physical environments, this paper presents the development and training of a robotic system designed to navigate and solve a labyrinth game using model-based reinforcement learning techniques.
Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization
Previous work upper bounds the posterior variance over values by solving a so-called uncertainty Bellman equation (UBE), but the over-approximation may result in inefficient exploration.
Regularity as Intrinsic Reward for Free Play
Our generalized formulation of Regularity as Intrinsic Reward (RaIR) allows us to operationalize it within model-based reinforcement learning.
LanGWM: Language Grounded World Model
Furthermore, our proposed technique of explicit language-grounded visual representation learning has the potential to improve models for human-robot interaction because our extracted visual features are language grounded.
Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learning
The proposed methodology is based on a machine learning approach that has recently set benchmark results in various domains: model-based reinforcement learning.
