General Reinforcement Learning
34 papers with code • 6 benchmarks • 7 datasets
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Most implemented papers
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.
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
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
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
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
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
Numerous past works have tackled the problem of task-driven navigation.
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
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 to Incentivize Other Learning Agents
The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years.