A survey on model-based reinforcement learning
Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …
making problems via a trial-and-error approach. Errors are always undesirable in real-world …
Bootstrapped transformer for offline reinforcement learning
K Wang, H Zhao, X Luo, K Ren… - Advances in Neural …, 2022 - proceedings.neurips.cc
Offline reinforcement learning (RL) aims at learning policies from previously collected static
trajectory data without interacting with the real environment. Recent works provide a novel …
trajectory data without interacting with the real environment. Recent works provide a novel …
Understanding world models through multi-step pruning policy via reinforcement learning
In model-based reinforcement learning, the conventional approach to addressing world
model bias is to use gradient optimization methods. However, using a singular policy from …
model bias is to use gradient optimization methods. However, using a singular policy from …
Empirical prior based probabilistic inference neural network for policy learning
Y Li, S Guo, Z Gan - Information Sciences, 2022 - Elsevier
Reinforcement learning is very much democratized for autonomous control of an unknown
dynamics system. However, low data efficiency is a practical concern in physical systems …
dynamics system. However, low data efficiency is a practical concern in physical systems …
[PDF][PDF] Automated cryptocurrency trading bot implementing DRL
ABSTRACT A year ago, one thousand USD invested in Bitcoin (BTC) alone would have
appreciated to three thousand five hundred USD. Deep reinforcement learning (DRL) recent …
appreciated to three thousand five hundred USD. Deep reinforcement learning (DRL) recent …
RITA: Boost driving simulators with realistic interactive traffic flow
High-quality traffic flow generation is the core module in building simulators for autonomous
driving. However, the majority of available simulators are incapable of replicating traffic …
driving. However, the majority of available simulators are incapable of replicating traffic …
Implicit Safe Set Algorithm for Provably Safe Reinforcement Learning
Deep reinforcement learning (DRL) has demonstrated remarkable performance in many
continuous control tasks. However, a significant obstacle to the real-world application of …
continuous control tasks. However, a significant obstacle to the real-world application of …
RITA: Boost Autonomous Driving Simulators with Realistic Interactive Traffic Flow
High-quality traffic flow generation is the core module in building simulators for autonomous
driving. However, the majority of available simulators are incapable of replicating traffic …
driving. However, the majority of available simulators are incapable of replicating traffic …
Adversarially Trained Environment Models Are Effective Policy Evaluators and Improvers-An Application to Information Retrieval
The essence of information retrieval (IR) is to find the most useful information items (or
documents) according to the user's information need and present the items to the users in …
documents) according to the user's information need and present the items to the users in …
Modelling Pedestrians in Autonomous Vehicle Testing
M Priisalu - 2023 - portal.research.lu.se
Realistic modelling of pedestrians in Autonomous Vehicles (AV) s and AV testing is crucial
to avoid lethal collisions in deployment. The majority of AV trajectory forecasting literature do …
to avoid lethal collisions in deployment. The majority of AV trajectory forecasting literature do …