Transfer Reinforcement Learning

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

14 shown of 14 papers with code (41 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
  19 Nov 2015 3 repositories listed
  The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent.
• MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics
  28 Sep 2019 2 repositories listed
  Transfer reinforcement learning (RL) aims at improving the learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks.
• VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation
  18 Aug 2019 2 repositories listed
  In this paper, we show how novel transfer reinforcement learning techniques can be applied to the complex task of target driven navigation using the photorealistic AI2THOR simulator.
• Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data
  6 Nov 2024 1 repository listed
  However, with prior information on the degree of the dynamics shift, we design HySRL, a transfer algorithm that achieves problem-dependent sample complexity and outperforms pure online RL.
• Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations
  5 Feb 2022 1 repository listed
  In this paper, we approach the task of transfer learning between domains that differ in action spaces.
• AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
  6 Jul 2021 1 repository listed Syntology ran 0 of 7 samples · 7 unverified
  We show that by explicitly leveraging this compact representation to encode changes, we can efficiently adapt the policy to the target domain, in which only a few samples are needed and further policy optimization is…
• Scalable Multiagent Driving Policies For Reducing Traffic Congestion
  26 Feb 2021 1 repository listed
  Next, we propose a modular transfer reinforcement learning approach, and use it to scale up a multiagent driving policy to outperform human-like traffic and existing approaches in a simulated realistic scenario, which…
• Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation
  10 Feb 2021 1 repository listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  To address this issue, we propose a two-stage RL agent that first learns a latent unified state representation (LUSR) which is consistent across multiple domains in the first stage, and then do RL training in one source…
• Action Priors for Large Action Spaces in Robotics
  11 Jan 2021 1 repository listed
  This paper proposes an alternative approach where the solutions of previously solved tasks are used to produce an action prior that can facilitate exploration in future tasks.
• gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo
  14 Mar 2019 1 repository listed
  This paper presents an upgraded, real world application oriented version of gym-gazebo, the Robot Operating System (ROS) and Gazebo based Reinforcement Learning (RL) toolkit, which complies with OpenAI Gym.
• Hardware Conditioned Policies for Multi-Robot Transfer Learning
  24 Nov 2018 1 repository listed
  In tasks where knowing the agent dynamics is important for success, we learn an embedding for robot hardware and show that policies conditioned on the encoding of hardware tend to generalize and transfer well.
• Deep Transfer Reinforcement Learning for Text Summarization
  15 Oct 2018 1 repository listed
  Deep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets.
• Universal Planning Networks
  2 Apr 2018 1 repository listed
  We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images.
• Shapechanger: Environments for Transfer Learning
  15 Sep 2017 1 repository listed
  We present Shapechanger, a library for transfer reinforcement learning specifically designed for robotic tasks.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.