Benchmarks
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Subtasks
Datasets
Latest papers with code
End-to-End Egospheric Spatial Memory
• ivy-dl/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.
GENERAL REINFORCEMENT LEARNING IMITATION LEARNING ROBOT NAVIGATION SEMANTIC SEGMENTATION VISUAL NAVIGATION
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
Align-RUDDER outperforms competitors on complex artificial tasks with delayed reward and few demonstrations.
GENERAL REINFORCEMENT LEARNING MINECRAFT MULTIPLE SEQUENCE ALIGNMENT SAFE EXPLORATION
Data-Efficient Reinforcement Learning with Self-Predictive Representations
We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation.
DATA AUGMENTATION FUTURE PREDICTION GENERAL REINFORCEMENT LEARNING SELF-SUPERVISED LEARNING
Counterfactual Data Augmentation using Locally Factored Dynamics
Many dynamic processes, including common scenarios in robotic control and reinforcement learning (RL), involve a set of interacting subprocesses.
DATA AUGMENTATION GENERAL REINFORCEMENT LEARNING MULTI-GOAL REINFORCEMENT LEARNING
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation.
FPS GAMES GENERAL REINFORCEMENT LEARNING MULTI-AGENT REINFORCEMENT LEARNING
Learning to Incentivize Other Learning Agents
NeurIPS 2020
• 011235813/lio
•
The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years.
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.
ATARI GAMES DECISION MAKING FEATURE IMPORTANCE GENERAL REINFORCEMENT LEARNING
Stabilizing Transformers for Reinforcement Learning
ICML 2020
• lucidrains/x-transformers
• 
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.
GENERAL REINFORCEMENT LEARNING LANGUAGE MODELLING MACHINE TRANSLATION
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.