S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning
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Summary
This paper provides a set of environments, data generators, robotic control tasks, metrics and tools to facilitate iterative state representation learning and evaluation in reinforcement learning settings.
- Type
- preprint
- Published
- 2018-09-25
- Cited by
- 35
- References
- 27
- Access
- Open access
- OpenAlex
- https://openalex.org/W2894485595
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:52821566
Keywords
Toolbox, Computer science, Representation (politics), State (computer science), Artificial intelligence
References
- Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
- Learning state representations with robotic priors
- Autonomous reinforcement learning on raw visual input data in a real world application
- Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES)
- Human-level control through deep reinforcement learning
- MuJoCo: A physics engine for model-based control
- Deep Residual Learning for Image Recognition
- State Representation Learning in Robotics: Using Prior Knowledge about Physical Interaction
- Closing the learning-planning loop with predictive state representations
- Time-Contrastive Networks: Self-Supervised Learning from Multi-view Observation
- Curiosity-Driven Exploration by Self-Supervised Prediction
- PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations
- Unsupervised state representation learning with robotic priors: a robustness benchmark
- State Representation Learning for Control: An Overview
- Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
- Simple random search provides a competitive approach to reinforcement learning
- World Models
- Decoupling Dynamics and Reward for Transfer Learning
- Learning robotic perception through prior knowledge
- Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Cited by
- Continual State Representation Learning for Reinforcement Learning using Generative Replay
- Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Active Tasks
- Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
- S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay
- Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer
- Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments
- DisCoRL: Continual Reinforcement Learning via Policy Distillation
- State Representation Learning from Demonstration
- Deep unsupervised state representation learning with robotic priors: a robustness analysis
- DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics
- PyRoboLearn: A Python Framework for Robot Learning Practitioners
- Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
- Benchmarking Unsupervised Representation Learning for Continuous Control
- Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes
- Bootstrapping Robotic Ecological Perception with Exploration and Interactions
- Explainability in Deep Reinforcement Learning
- Unsupervised representation learning in interactive environments
- Robust Policies via Mid-Level Visual Representations: An Experimental Study in Manipulation and Navigation
- Towards Learning Controllable Representations of Physical Systems
- Demonstration Guided Actor-Critic Deep Reinforcement Learning for Fast Teaching of Robots in Dynamic Environments
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