General Reinforcement Learning

35 papers with code • 6 benchmarks • 7 datasets

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Use these libraries to find General Reinforcement Learning models and implementations

Most implemented papers

Learning to Incentivize Other Learning Agents

011235813/lio NeurIPS 2020

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

chandar-lab/LoCA 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 15 Feb 2021

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

data-and-decision-lab/defix 29 Oct 2022

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

aslanides/aixijs 3 Mar 2017

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

aslanides/aixijs 22 May 2017

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

innixma/dex 19 Jun 2017

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

fabiopardo/tonic ICML 2018

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

valeoai/rainbow-iqn-apex 13 Aug 2019

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

HuTobias/HIGHLIGHTS-LRP 18 May 2020

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.