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
End-to-End Egospheric Spatial Memory
ivy-dl/memory
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Spatial memory, or the ability to remember and recall specific locations and objects, is central to autonomous agents' ability to carry out tasks in real environments.
Interactive Learning from Activity Description
khanhptnk/iliad
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We present a novel interactive learning protocol that enables training request-fulfilling agents by verbally describing their activities.
Learning to Represent Action Values as a Hypergraph on the Action Vertices
atavakol/action-hypergraph-networks
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ICLR 2021
To test this, we set forth the action hypergraph networks framework -- a class of functions for learning action representations in multi-dimensional discrete action spaces with a structural inductive bias.
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
For such complex tasks, the recently proposed RUDDER uses reward redistribution to leverage steps in the Q-function that are associated with accomplishing sub-tasks.
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).
Data-Efficient Reinforcement Learning with Self-Predictive Representations
We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation.
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
chandar-lab/LoCA
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NeurIPS 2020
For example, the common single-task sample-efficiency metric conflates improvements due to model-based learning with various other aspects, such as representation learning, making it difficult to assess true progress on model-based RL.
Counterfactual Data Augmentation using Locally Factored Dynamics
Many dynamic processes, including common scenarios in robotic control and reinforcement learning (RL), involve a set of interacting subprocesses.
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
opendilab/DI-engine
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ICML 2020
In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation.
Learning to Incentivize Other Learning Agents
011235813/lio
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NeurIPS 2020
The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years.
