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 with no code
D3PG: Dirichlet DDPG for Task Partitioning and Offloading With Constrained Hybrid Action Space in Mobile-Edge Computing
Mobile-edge computing (MEC) has been regarded as a promising paradigm to reduce service latency for data processing in the Internet of Things (IoT) by provisioning computing resources at the network edges.
Abstractions of General Reinforcement Learning
The field of artificial intelligence (AI) is devoted to the creation of artificial decision-makers that can perform (at least) on par with the human counterparts on a domain of interest.
Reducing Planning Complexity of General Reinforcement Learning with Non-Markovian Abstractions
A distinguishing feature of ESA is that it proves an upper bound of $O\left(\varepsilon^{-A} \cdot (1-\gamma)^{-2A}\right)$ on the number of states required for the surrogate MDP (where $A$ is the number of actions, $\gamma$ is the discount-factor, and $\varepsilon$ is the optimality-gap) which holds \emph{uniformly} for \emph{all} domains.
Superior Performance with Diversified Strategic Control in FPS Games Using General Reinforcement Learning
We provide comprehensive analysis and experiments to elaborate the effect of each component in affecting the agent performance, and demonstrate that the proposed and adopted techniques are important to achieve superior performance in general end-to-end FPS games.
$\sbf{\delta^2}$-exploration for Reinforcement Learning
Effectively tackling the \emph{exploration-exploitation dilemma} is still a major challenge in reinforcement learning.
A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning
To apply value-based methods to CRL, a recent groundbreaking line of game-theoretic approaches uses the mixed policy that randomizes among a set of carefully generated policies to converge to the desired constraint-satisfying policy.
Nearest-Neighbor-based Collision Avoidance for Quadrotors via Reinforcement Learning
Collision avoidance algorithms are of central interest to many drone applications.
FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT
Results obtained on these two applications validate the generality of FaiR-IoT and its ability to provide a personalized experience while enhancing the system's performance by 40%-60% compared to non-personalized systems and enhancing the fairness of the multi-human systems by 1. 5 orders of magnitude.
A State Representation Dueling Network for Deep Reinforcement Learning
In recent years there have been many successes in boosting the performance of Deep Q-Networks (DQN).
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.
