Reinforcement Learning Algorithms: Analysis and Applications /
This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on...
| Corporate Author: | |
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| Other Authors: | , , , , |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2021.
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| Edition: | 1st ed. 2021. |
| Series: | Studies in Computational Intelligence,
883 |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- Prediction Error and Actor-Critic Hypotheses in the Brain
- Reviewing on-policy / off-policy critic learning in the context of Temporal Differences and Residual Learning
- Reward Function Design in Reinforcement Learning
- Exploration Methods In Sparse Reward Environments
- A Survey on Constraining Policy Updates Using the KL Divergence
- Fisher Information Approximations in Policy Gradient Methods
- Benchmarking the Natural gradient in Policy Gradient Methods and Evolution Strategies
- Information-Loss-Bounded Policy Optimization
- Persistent Homology for Dimensionality Reduction
- Model-free Deep Reinforcement Learning - Algorithms and Applications
- Actor vs Critic
- Bring Color to Deep Q-Networks
- Distributed Methods for Reinforcement Learning
- Model-Based Reinforcement Learning
- Challenges of Model Predictive Control in a Black Box Environment
- Control as Inference?