… We design the joint space trajectory to have the same form as the trajectories in the … with
designed trajectories, and the policy of our MBORL algorithm is safely optimized offline. This …

… When collecting data in the conservative MDP, we collect h-length truncated trajectories
starting from states in the original offline dataset. By collecting data this way, we are able to …

… Offline or batch reinforcement learning seeks to learn a near-optimal policy using history …
To counter the insufficient coverage and sample scarcity of many offline datasets, the principle …

… -optimal policies (eg, robotic control and recommendation systems). To simulate this scenario,
we collect the offline dataset with a sub-optimal … evaluate offline RL algorithms by training …

… We study the offline safe RL problem from a novel multi-objective optimization perspective
… maximum-reward Pareto optimal trajectory with cost less than κ. Then we append the new …

… trajectory-based preference optimization. We directly generate preferred trajectory data
for preference optimization, … We can compare two trajectories based on success or time to …

… , the risk to crash the production machines if optimizing production processes, or the risk to
loose … In Offline RL, we assume that a dataset P of trajectories is provided. A single trajectory …

… , latent offline modelbased policy optimization (LOMPO), which enables … on offline RL in
high-dimensional POMDPs, where the agent has access to the fixed dataset Denv of trajectories, …

… offline RL setting can make full use of the ability of deep networks to extract the optimal policy
from a large amount of offline … discrepancy between the offline training data and the target …

… [19] samples action sequences from the CVAE when they perform trajectory optimization
with the learned latent dynamics model. Krupnik et al. [20] extended the previous method to …