Cycle-Consistent World Models for Domain Independent Latent Imagination
- URL: http://arxiv.org/abs/2110.00808v1
- Date: Sat, 2 Oct 2021 13:55:50 GMT
- Title: Cycle-Consistent World Models for Domain Independent Latent Imagination
- Authors: Sidney Bender, Tim Joseph, Marius Zoellner
- Abstract summary: High costs and risks make it hard to train autonomous cars in the real world.
We propose a novel model-based reinforcement learning approach called Cycleconsistent World Models.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: End-to-end autonomous driving seeks to solve the perception, decision, and
control problems in an integrated way, which can be easier to generalize at
scale and be more adapting to new scenarios. However, high costs and risks make
it very hard to train autonomous cars in the real world. Simulations can
therefore be a powerful tool to enable training. Due to slightly different
observations, agents trained and evaluated solely in simulation often perform
well there but have difficulties in real-world environments. To tackle this
problem, we propose a novel model-based reinforcement learning approach called
Cycleconsistent World Models. Contrary to related approaches, our model can
embed two modalities in a shared latent space and thereby learn from samples in
one modality (e.g., simulated data) and be used for inference in different
domain (e.g., real-world data). Our experiments using different modalities in
the CARLA simulator showed that this enables CCWM to outperform
state-of-the-art domain adaptation approaches. Furthermore, we show that CCWM
can decode a given latent representation into semantically coherent
observations in both modalities.
Related papers
- Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models [60.87795376541144]
A world model is a neural network capable of predicting an agent's next state given past states and actions.
During end-to-end training, our policy learns how to recover from errors by aligning with states observed in human demonstrations.
We present qualitative and quantitative results, demonstrating significant improvements upon prior state of the art in closed-loop testing.
arXiv Detail & Related papers (2024-09-25T06:48:25Z) - Probing Multimodal LLMs as World Models for Driving [72.18727651074563]
We look at the application of Multimodal Large Language Models (MLLMs) in autonomous driving.
Despite advances in models like GPT-4o, their performance in complex driving environments remains largely unexplored.
arXiv Detail & Related papers (2024-05-09T17:52:42Z) - Physics-informed reinforcement learning via probabilistic co-adjustment
functions [3.6787556334630334]
We introduce co-kriging adjustments (CKA) and ridge regression adjustment (RRA) as novel ways to combine the advantages of both approaches.
Our adjustment methods are based on an auto-regressive AR1 co-kriging model that we integrate with GP priors.
arXiv Detail & Related papers (2023-09-11T12:10:19Z) - Pre-training Contextualized World Models with In-the-wild Videos for
Reinforcement Learning [54.67880602409801]
In this paper, we study the problem of pre-training world models with abundant in-the-wild videos for efficient learning of visual control tasks.
We introduce Contextualized World Models (ContextWM) that explicitly separate context and dynamics modeling.
Our experiments show that in-the-wild video pre-training equipped with ContextWM can significantly improve the sample efficiency of model-based reinforcement learning.
arXiv Detail & Related papers (2023-05-29T14:29:12Z) - Model-Based Reinforcement Learning with Isolated Imaginations [61.67183143982074]
We propose Iso-Dream++, a model-based reinforcement learning approach.
We perform policy optimization based on the decoupled latent imaginations.
This enables long-horizon visuomotor control tasks to benefit from isolating mixed dynamics sources in the wild.
arXiv Detail & Related papers (2023-03-27T02:55:56Z) - Dream to Explore: Adaptive Simulations for Autonomous Systems [3.0664963196464448]
We tackle the problem of learning to control dynamical systems by applying Bayesian nonparametric methods.
By employing Gaussian processes to discover latent world dynamics, we mitigate common data efficiency issues observed in reinforcement learning.
Our algorithm jointly learns a world model and policy by optimizing a variational lower bound of a log-likelihood.
arXiv Detail & Related papers (2021-10-27T04:27:28Z) - DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain
Randomization Reinforcement Learning [4.040937987024427]
Domain Randomization(DR) is a methodology that can bridge this gap with little or no real-world data.
An adversarial model is put forward to robustify DeepRL-based autonomous vehicles trained in simulation.
arXiv Detail & Related papers (2021-07-25T09:15:46Z) - TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation.
In particular, we leverage an implicit latent variable model to parameterize a joint actor policy.
We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
arXiv Detail & Related papers (2021-01-17T00:29:30Z) - From Simulation to Real World Maneuver Execution using Deep
Reinforcement Learning [69.23334811890919]
Deep Reinforcement Learning has proved to be able to solve many control tasks in different fields, but the behavior of these systems is not always as expected when deployed in real-world scenarios.
This is mainly due to the lack of domain adaptation between simulated and real-world data together with the absence of distinction between train and test datasets.
We present a system based on multiple environments in which agents are trained simultaneously, evaluating the behavior of the model in different scenarios.
arXiv Detail & Related papers (2020-05-13T14:22:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.