Transfer Reinforcement Learning
13 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Transfer Reinforcement Learning
bipedal-skills
Latest papers with no code
Modular Transfer Learning with Transition Mismatch Compensation for Excessive Disturbance Rejection
In this paper, we propose a transfer learning framework that adapts a control policy for excessive disturbance rejection of an underwater robot under dynamics model mismatch.
Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning
This article proposes a transfer reinforcement learning (RL) based adaptive energy managing approach for a hybrid electric vehicle (HEV) with parallel topology.
Transfer Reinforcement Learning under Unobserved Contextual Information
Then, the goal is to transfer this experience, excluding the underlying contextual information, to a learner agent that does not have access to the environmental context, so that they can learn a control policy using fewer samples.
Universal Successor Features for Transfer Reinforcement Learning
Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks.
How Does an Approximate Model Help in Reinforcement Learning?
In particular, we provide an algorithm that uses $\widetilde{O}(N/(1-\gamma)^3/\varepsilon^2)$ samples in a generative model to learn an $\varepsilon$-optimal policy, where $\gamma$ is the discount factor and $N$ is the number of near-optimal actions in the approximate model.
Federated Transfer Reinforcement Learning for Autonomous Driving
Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL models typically involves in a multi-step process: pre-training RL models on simulators, uploading the pre-trained model to real-life robots, and fine-tuning the weight parameters on robot vehicles.
Generalization in Transfer Learning
Furthermore, we increase the generalization capacity in widely used transfer learning benchmarks by using maximum entropy regularization, different critic methods, and curriculum learning in an adversarial setup.
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules
The resulting method is flexible and it can be easily incorporated to any standard off-policy and on-policy algorithms, such as those based on temporal differences and policy gradients.
VPE: Variational Policy Embedding for Transfer Reinforcement Learning
The low-dimensional space, and master policy found by our method enables policies to quickly adapt to new environments.
Universal Successor Representations for Transfer Reinforcement Learning
The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks.
