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Found 6 papers, 5 papers with code
Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft
An important challenge in reinforcement learning is training agents that can solve a wide variety of tasks.
Phasic Policy Gradient
We introduce Phasic Policy Gradient (PPG), a reinforcement learning framework which modifies traditional on-policy actor-critic methods by separating policy and value function training into distinct phases.
Ranked #1 on
Reinforcement Learning (RL)
on ProcGen
(using extra training data)
Quantifying Generalization in Reinforcement Learning
In this paper, we investigate the problem of overfitting in deep reinforcement learning.
Exploration by Random Network Distillation
In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods.
Gotta Learn Fast: A New Benchmark for Generalization in RL
In this report, we present a new reinforcement learning (RL) benchmark based on the Sonic the Hedgehog (TM) video game franchise.
Proximal Policy Optimization Algorithms
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent.
