Transfer Reinforcement Learning
13 papers with code • 0 benchmarks • 1 datasets
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bipedal-skills
Latest papers with no code
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation
Multi-Agent Reinforcement Learning (MARL) algorithms are widely adopted in tackling complex tasks that require collaboration and competition among agents in dynamic Multi-Agent Systems (MAS).
Value Explicit Pretraining for Learning Transferable Representations
We propose Value Explicit Pretraining (VEP), a method that learns generalizable representations for transfer reinforcement learning.
Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework
In this work, we investigate the potential of improving multi-task training and also leveraging it for transferring in the reinforcement learning setting.
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer
Then, we propose a generic two-stage algorithm, MLEMTRL, to address the MTRL problem in discrete and continuous settings.
Provably Sample-Efficient RL with Side Information about Latent Dynamics
We study reinforcement learning (RL) in settings where observations are high-dimensional, but where an RL agent has access to abstract knowledge about the structure of the state space, as is the case, for example, when a robot is tasked to go to a specific room in a building using observations from its own camera, while having access to the floor plan.
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing
In this paper, we propose a deep transfer reinforcement learning (DTRL) scheme for joint radio and cache resource allocation to serve 5G RAN slicing.
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning
We assume that this context is not accessible to a learner agent who can only observe the expert data.
Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning
We note a surprisingly late adoption of deep learning that starts in 2018.
Transfer Reinforcement Learning across Homotopy Classes
The ability for robots to transfer their learned knowledge to new tasks -- where data is scarce -- is a fundamental challenge for successful robot learning.
Learn Dynamic-Aware State Embedding for Transfer Learning
In this setting, the MDP dynamic is a good knowledge to transfer, which can be inferred by uniformly random policy.
