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
Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework
The performance of many algorithms in the fields of hard combinatorial problem solving, machine learning or AI in general depends on parameter tuning.
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
Specifically, we augment strategy summaries that extract important trajectories of states from simulations of the agent with saliency maps which show what information the agent attends to.
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.
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.
Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field
In the Arcade Learning Environment (ALE), small changes in environment parameters such as stochasticity or the maximum allowed play time can lead to very different performance.
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
microsoft/logrl
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NeurIPS 2019
In an effort to better understand the different ways in which the discount factor affects the optimization process in reinforcement learning, we designed a set of experiments to study each effect in isolation.
Learning Exploration Policies for Navigation
taochenshh/exp4nav
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ICLR 2019
Numerous past works have tackled the problem of task-driven navigation.
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.
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
The game of chess is the most widely-studied domain in the history of artificial intelligence.
Time Limits in Reinforcement Learning
fabiopardo/tonic
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ICML 2018
In case (ii), the time limits are not part of the environment and are only used to facilitate learning.
