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Found 7 papers, 3 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.
Contextual Imagined Goals for Self-Supervised Robotic Learning
When the robot's environment and available objects vary, as they do in most open-world settings, the robot must propose to itself only those goals that it can accomplish in its present setting with the objects that are at hand.
REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning
We envision REPLAB as a framework for reproducible research across manipulation tasks, and as a step in this direction, we define a template for a grasping benchmark consisting of a task definition, evaluation protocol, performance measures, and a dataset of 92k grasp attempts.
Visual Reinforcement Learning with Imagined Goals
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.
Composable Deep Reinforcement Learning for Robotic Manipulation
Second, we show that policies learned with soft Q-learning can be composed to create new policies, and that the optimality of the resulting policy can be bounded in terms of the divergence between the composed policies.
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
TDMs combine the benefits of model-free and model-based RL: they leverage the rich information in state transitions to learn very efficiently, while still attaining asymptotic performance that exceeds that of direct model-based RL methods.
Uncertainty-Aware Reinforcement Learning for Collision Avoidance
However, practical deployment of reinforcement learning methods must contend with the fact that the training process itself can be unsafe for the robot.
