Current Trends in Reinforcement Learning for Control and Robotics

The field of reinforcement learning is moving towards more robust and adaptive methods for control and robotics. Notable developments include the use of deep neural networks to enhance the performance of conventional reinforcement learning, and the extension of these methods to continuous control algorithms. There is also a growing interest in multi-agent reinforcement learning and its application to real-world systems. Researchers are working on developing more principled assessments of performance and adaptability in these systems. Some particularly noteworthy papers include:

  • Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots, which proposes an approach for fine-tuning Deep RL policies using Evolutionary Strategies to enhance control performance for underactuated robots.
  • Adaptability in Multi-Agent Reinforcement Learning: A Framework and Unified Review, which introduces a framework for evaluating the reliability of MARL algorithms under shifting conditions.

Sources

Deep Reinforcement Learning in Applied Control: Challenges, Analysis, and Insights

Enhancing Parameter Control Policies with State Information

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots

Adaptability in Multi-Agent Reinforcement Learning: A Framework and Unified Review

High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization

Evaluating Reinforcement Learning Algorithms for Navigation in Simulated Robotic Quadrupeds: A Comparative Study Inspired by Guide Dog Behaviour

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