General Reinforcement Learning
35 papers with code • 6 benchmarks • 7 datasets
Libraries
Use these libraries to find General Reinforcement Learning models and implementationsMost implemented papers
Learning to Incentivize Other Learning Agents
The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years.
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
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
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.
DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving
In this paper, we present a Reinforcement Learning (RL) based methodology to DEtect and FIX (DeFIX) failures of an Imitation Learning (IL) agent by extracting infraction spots and re-constructing mini-scenarios on these infraction areas to train an RL agent for fixing the shortcomings of the IL approach.
Generalised Discount Functions applied to a Monte-Carlo AImu Implementation
We have added to the GRL simulation platform AIXIjs the functionality to assign an agent arbitrary discount functions, and an environment which can be used to determine the effect of discounting on an agent's policy.
AIXIjs: A Software Demo for General Reinforcement Learning
The universal Bayesian agent AIXI (Hutter, 2005) is a model of a maximally intelligent agent, and plays a central role in the sub-field of general reinforcement learning (GRL).
Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning
This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems.
Time Limits in Reinforcement Learning
In case (ii), the time limits are not part of the environment and are only used to facilitate learning.
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.
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.