This paper presents two improvements over the existing HER algorithm, which prioritize virtual goals from which the agent will learn more valuable information, and reduces existing bias in HER by the removal of misleading samples.
Authors
A. Biess
2 papers
Binyamin Manela
1 papers
References16 items
1
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
2
Energy-Based Hindsight Experience Prioritization
3
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
4
Proximal Policy Optimization Algorithms
5
Hindsight Experience Replay
6
Understanding deep learning requires rethinking generalization
7
OpenAI Gym
8
Prioritized Experience Replay
9
Continuous control with deep reinforcement learning
10
Universal Value Function Approximators
11
Playing Atari with Deep Reinforcement Learning
12
Reinforcement learning in robotics: A survey
13
MuJoCo: A physics engine for model-based control
14
Deep Learning
15
Reinforcement Learning and the Reward Engineering Principle