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Found 4 papers, 1 papers with code
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning
In this work, we show that an uncertainty aware classifier can solve challenging reinforcement learning problems by both encouraging exploration and provided directed guidance towards positive outcomes.
Reinforcement Learning with Bayesian Classifiers: Efficient Skill Learning from Outcome Examples
In this work, we study a more tractable class of reinforcement learning problems defined by data that provides examples of successful outcome states.
Learning to Reach Goals via Iterated Supervised Learning
Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards.
Multi-Goal Reinforcement Learning
Reinforcement Learning (RL)
Learning to Reach Goals Without Reinforcement Learning
By maximizing the likelihood of good actions provided by an expert demonstrator, supervised imitation learning can produce effective policies without the algorithmic complexities and optimization challenges of reinforcement learning, at the cost of requiring an expert demonstrator -- typically a person -- to provide the demonstrations.
