Meta Reinforcement Learning
88 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Meta Reinforcement Learning
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Libraries
Use these libraries to find Meta Reinforcement Learning models and implementations
Meta-World Benchmark
Latest papers with no code
On Task-Relevant Loss Functions in Meta-Reinforcement Learning and Online LQR
Designing a competent meta-reinforcement learning (meta-RL) algorithm in terms of data usage remains a central challenge to be tackled for its successful real-world applications.
Adaptive Agents and Data Quality in Agent-Based Financial Markets
As a baseline, we populate ABMMS with simple trading agents and investigate properties of the generated data.
An MRL-Based Design Solution for RIS-Assisted MU-MIMO Wireless System under Time-Varying Channels
Our approach improves the sum rate by more than 60% under time-varying CSI assumption while maintaining the advantages of typical DRL-based solutions.
Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning
We introduce PACOH-RL, a novel model-based Meta-Reinforcement Learning (Meta-RL) algorithm designed to efficiently adapt control policies to changing dynamics.
An introduction to reinforcement learning for neuroscience
We then provide an introduction to deep reinforcement learning with examples of how these methods have been used to model different learning phenomena in the systems neuroscience literature, such as meta-reinforcement learning (Wang et al., 2018) and distributional reinforcement learning (Dabney et al., 2020).
Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imagination
Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks.
Hypothesis Network Planned Exploration for Rapid Meta-Reinforcement Learning Adaptation
Meta Reinforcement Learning (Meta RL) trains agents that adapt to fast-changing environments and tasks.
Emergence of Collective Open-Ended Exploration from Decentralized Meta-Reinforcement Learning
We further find that the agents learned collective exploration strategies extend to an open ended task setting, allowing them to solve task trees of twice the depth compared to the ones seen during training.
Neurosymbolic Meta-Reinforcement Lookahead Learning Achieves Safe Self-Driving in Non-Stationary Environments
In the area of learning-driven artificial intelligence advancement, the integration of machine learning (ML) into self-driving (SD) technology stands as an impressive engineering feat.
Robust Driving Policy Learning with Guided Meta Reinforcement Learning
Although deep reinforcement learning (DRL) has shown promising results for autonomous navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior policy to control social vehicles in the training environment.
