Learning Control Policies for Variable Objectives from Offline Data
- URL: http://arxiv.org/abs/2308.06127v1
- Date: Fri, 11 Aug 2023 13:33:59 GMT
- Title: Learning Control Policies for Variable Objectives from Offline Data
- Authors: Marc Weber, Phillip Swazinna, Daniel Hein, Steffen Udluft, and Volkmar
Sterzing
- Abstract summary: We introduce a conceptual extension for model-based policy search methods, called variable objective policy (VOP)
We demonstrate that by altering the objectives passed as input to the policy, users gain the freedom to adjust its behavior or re-balance optimization targets at runtime.
- Score: 2.7174376960271154
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. In this paper, we introduce a conceptual
extension for model-based policy search methods, called variable objective
policy (VOP). With this approach, policies are trained to generalize
efficiently over a variety of objectives, which parameterize the reward
function. We demonstrate that by altering the objectives passed as input to the
policy, users gain the freedom to adjust its behavior or re-balance
optimization targets at runtime, without need for collecting additional
observation batches or re-training.
Related papers
- Policy-regularized Offline Multi-objective Reinforcement Learning [11.58560880898882]
We extend the offline policy-regularized method, a widely-adopted approach for single-objective offline RL problems, into the multi-objective setting.
We propose two solutions to this problem: 1) filtering out preference-inconsistent demonstrations via approximating behavior preferences, and 2) adopting regularization techniques with high policy expressiveness.
arXiv Detail & Related papers (2024-01-04T12:54:10Z) - Statistically Efficient Variance Reduction with Double Policy Estimation
for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning [53.97273491846883]
We propose DPE: an RL algorithm that blends offline sequence modeling and offline reinforcement learning with Double Policy Estimation.
We validate our method in multiple tasks of OpenAI Gym with D4RL benchmarks.
arXiv Detail & Related papers (2023-08-28T20:46:07Z) - Reparameterized Policy Learning for Multimodal Trajectory Optimization [61.13228961771765]
We investigate the challenge of parametrizing policies for reinforcement learning in high-dimensional continuous action spaces.
We propose a principled framework that models the continuous RL policy as a generative model of optimal trajectories.
We present a practical model-based RL method, which leverages the multimodal policy parameterization and learned world model.
arXiv Detail & Related papers (2023-07-20T09:05:46Z) - Imitating Graph-Based Planning with Goal-Conditioned Policies [72.61631088613048]
We present a self-imitation scheme which distills a subgoal-conditioned policy into the target-goal-conditioned policy.
We empirically show that our method can significantly boost the sample-efficiency of the existing goal-conditioned RL methods.
arXiv Detail & Related papers (2023-03-20T14:51:10Z) - Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in
Latent Space [76.46113138484947]
General-purpose robots require diverse repertoires of behaviors to complete challenging tasks in real-world unstructured environments.
To address this issue, goal-conditioned reinforcement learning aims to acquire policies that can reach goals for a wide range of tasks on command.
We propose Planning to Practice, a method that makes it practical to train goal-conditioned policies for long-horizon tasks.
arXiv Detail & Related papers (2022-05-17T06:58:17Z) - Latent-Variable Advantage-Weighted Policy Optimization for Offline RL [70.01851346635637]
offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions.
In practice, offline datasets are often heterogeneous, i.e., collected in a variety of scenarios.
We propose to leverage latent-variable policies that can represent a broader class of policy distributions.
Our method improves the average performance of the next best-performing offline reinforcement learning methods by 49% on heterogeneous datasets.
arXiv Detail & Related papers (2022-03-16T21:17:03Z) - A Regularized Implicit Policy for Offline Reinforcement Learning [54.7427227775581]
offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment.
We propose a framework that supports learning a flexible yet well-regularized fully-implicit policy.
Experiments and ablation study on the D4RL dataset validate our framework and the effectiveness of our algorithmic designs.
arXiv Detail & Related papers (2022-02-19T20:22:04Z) - Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep
Reinforcement Learning [9.014110264448371]
We propose a novel unsupervised learning approach named goal-conditioned policy with intrinsic motivation (GPIM)
GPIM jointly learns both an abstract-level policy and a goal-conditioned policy.
Experiments on various robotic tasks demonstrate the effectiveness and efficiency of our proposed GPIM method.
arXiv Detail & Related papers (2021-04-11T16:26:10Z)
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.