Benchmarks
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Subtasks
Datasets
Latest papers without code
Exact Reduction of Huge Action Spaces in General Reinforcement Learning
In this work we show how action-binarization in the non-MDP case can significantly improve Extreme State Aggregation (ESA) bounds.
BINARIZATION GENERAL REINFORCEMENT LEARNING PROTEIN FOLDING STARCRAFT
Causal variables from reinforcement learning using generalized Bellman equations
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.
Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors
The results show faster learning with the presented approach as opposed to learning the control policy from scratch for this new UAV design created by modifications in a conventional quadcopter, i. e., the addition of more degrees of freedom (4-actuators in conventional quadcopter to 8-actuators in tilt-rotor quadcopter).
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 develop a general reinforcement learning framework for mean field control (MFC) problems.
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.
GENERAL REINFORCEMENT LEARNING HIERARCHICAL REINFORCEMENT LEARNING TRANSFER LEARNING
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.