General Reinforcement Learning
35 papers with code • 6 benchmarks • 7 datasets
Benchmarks
These leaderboards are used to track progress in General Reinforcement Learning
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Libraries
Use these libraries to find General Reinforcement Learning models and implementations
ProcGen
Obstacle Tower
WLD
PackIt
Avalon
bipedal-skills
POPGym
Latest papers with no code
Reinforcement Learning of Causal Variables Using Mediation Analysis
To our knowledge, this is the first attempt to apply causal analysis in a reinforcement learning setting without strict restrictions on the number of states.
Learning as Reinforcement: Applying Principles of Neuroscience for More General Reinforcement Learning Agents
A significant challenge in developing AI that can generalize well is designing agents that learn about their world without being told what to learn, and apply that learning to challenges with sparse rewards.
Student/Teacher Advising through Reward Augmentation
Transfer learning is an important new subfield of multiagent reinforcement learning that aims to help an agent learn about a problem by using knowledge that it has gained solving another problem, or by using knowledge that is communicated to it by an agent who already knows the problem.
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning
We study infinite horizon discounted Mean Field Control (MFC) problems with common noise through the lens of Mean Field Markov Decision Processes (MFMDP).
Goal-Driven Sequential Data Abstraction
In the former one asks whether a machine can `understand' enough about the meaning of input data to produce a meaningful but more compact abstraction.
Compositional Transfer in Hierarchical Reinforcement Learning
The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements.
Variational Regret Bounds for Reinforcement Learning
This is the first variational regret bound for the general reinforcement learning setting.
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder
Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis.
Differential Temporal Difference Learning
Value functions derived from Markov decision processes arise as a central component of algorithms as well as performance metrics in many statistics and engineering applications of machine learning techniques.
Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays
Machine learning applications in medical imaging are frequently limited by the lack of quality labeled data.
