General Reinforcement Learning
27 papers with code • 6 benchmarks • 4 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
Most implemented papers
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
gcp/leela-zero
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The game of chess is the most widely-studied domain in the history of artificial intelligence.
OpenSpiel: A Framework for Reinforcement Learning in Games
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Action Branching Architectures for Deep Reinforcement Learning
atavakol/action-branching-agents
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This approach achieves a linear increase of the number of network outputs with the number of degrees of freedom by allowing a level of independence for each individual action dimension.
Gibson Env: Real-World Perception for Embodied Agents
StanfordVL/GibsonEnv
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CVPR 2018
Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly.
Stabilizing Transformers for Reinforcement Learning
opendilab/DI-engine
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ICML 2020
Harnessing the transformer's ability to process long time horizons of information could provide a similar performance boost in partially observable reinforcement learning (RL) domains, but the large-scale transformers used in NLP have yet to be successfully applied to the RL setting.
Adaptive Rational Activations to Boost Deep Reinforcement Learning
ml-research/rational_activations
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Latest insights from biology show that intelligence not only emerges from the connections between neurons but that individual neurons shoulder more computational responsibility than previously anticipated.
A Monte Carlo AIXI Approximation
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent.
Learning Exploration Policies for Navigation
taochenshh/exp4nav
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ICLR 2019
Numerous past works have tackled the problem of task-driven navigation.
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.
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.
