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Found 7 papers, 3 papers with code
Learning Control Policies for Variable Objectives from Offline Data
Offline reinforcement learning provides a viable approach to obtain advanced control strategies for dynamical systems, in particular when direct interaction with the environment is not available.
Automatic Trade-off Adaptation in Offline RL
Recently, offline RL algorithms have been proposed that remain adaptive at runtime.
User-Interactive Offline Reinforcement Learning
At the same time, offline RL algorithms are not able to tune their most important hyperparameter - the proximity of the learned policy to the original policy.
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning
Offline reinforcement learning (RL) Algorithms are often designed with environments such as MuJoCo in mind, in which the planning horizon is extremely long and no noise exists.
Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning
Recently developed offline reinforcement learning algorithms have made it possible to learn policies directly from pre-collected datasets, giving rise to a new dilemma for practitioners: Since the performance the algorithms are able to deliver depends greatly on the dataset that is presented to them, practitioners need to pick the right dataset among the available ones.
Behavior Constraining in Weight Space for Offline Reinforcement Learning
In offline reinforcement learning, a policy needs to be learned from a single pre-collected dataset.
Overcoming Model Bias for Robust Offline Deep Reinforcement Learning
State-of-the-art reinforcement learning algorithms mostly rely on being allowed to directly interact with their environment to collect millions of observations.
