307 citations · 868 across the 18 of their papers we have counts for
10 papers · 1 filter
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning
Kevin Li, Abhishek Gupta, Ashwin Reddy +4
Exploration in reinforcement learning is a challenging problem: in the worst case, the agent must search for high-reward states that could be hidden anywhere in the state space. Ca…
Offline Meta-Reinforcement Learning with Online Self-Supervision
Vitchyr H. Pong, Ashvin Nair, Laura Smith +2
Meta-reinforcement learning (RL) methods can meta-train policies that adapt to new tasks with orders of magnitude less data than standard RL, but meta-training itself is costly and…
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies
Soroush Nasiriany, Vitchyr H. Pong, Ashvin Nair +3
Can we use reinforcement learning to learn general-purpose policies that can perform a wide range of different tasks, resulting in flexible and reusable skills? Contextual policies…
Outcome-Driven Reinforcement Learning via Variational Inference
Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister +2
While reinforcement learning algorithms provide automated acquisition of optimal policies, practical application of such methods requires a number of design decisions, such as manu…
Planning with Goal-Conditioned Policies
Soroush Nasiriany, Vitchyr H. Pong, Steven Lin +1
Planning methods can solve temporally extended sequential decision making problems by composing simple behaviors. However, planning requires suitable abstractions for the states an…
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Vitchyr H. Pong, Murtaza Dalal, Steven Lin +3
Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills. Defining each skill with a manually-designed reward f…
Visual Reinforcement Learning with Imagined Goals
Ashvin Nair, Vitchyr Pong, Murtaza Dalal +3
For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore…
Composable Deep Reinforcement Learning for Robotic Manipulation
Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou +3
Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, mod…
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
Vitchyr Pong, Shixiang Gu, Murtaza Dalal +1
Model-free reinforcement learning (RL) is a powerful, general tool for learning complex behaviors. However, its sample efficiency is often impractically large for solving challengi…
Uncertainty-Aware Reinforcement Learning for Collision Avoidance
Gregory Kahn, Adam Villaflor, Vitchyr Pong +2
Reinforcement learning can enable complex, adaptive behavior to be learned automatically for autonomous robotic platforms. However, practical deployment of reinforcement learning m…