Reinforcement Learning

Reinforcement Learning (RL) endows robots with the ability to learn control policies through trial-and-error interactions rather than hand-coding behaviors. This page surveys core RL approaches, their robotic applications, and a curated set of learning resources and software tools.

Core RL Algorithms and Resources

• Value-Based Methods

  • Q-Learning & SARSA – Tabular methods for discrete state–action spaces

  • Sutton & Barto’s “Reinforcement Learning: An Introduction” (http://incompleteideas.net/book/the-book.html)
  • Deep Q-Networks (DQN) & Variants (Double DQN, Dueling DQN) – Neural-network approximators for high-dimensional inputs

  • OpenAI Baselines DQN implementation (https://github.com/openai/baselines)
• Policy-Gradient Methods

  • REINFORCE – Monte-Carlo policy search

  • Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) – Stable on-policy updates

  • OpenAI Spinning Up tutorials (https://spinningup.openai.com)
  • Actor-Critic (A2C, A3C) – Combines policy gradient with value estimates

• Continuous-Control Algorithms

  • Deep Deterministic Policy Gradient (DDPG) & Twin Delayed DDPG (TD3) – Off-policy actor-critic for continuous actions

  • Soft Actor-Critic (SAC) – Maximum-entropy RL for robustness

  • Stable Baselines3 implementations (https://github.com/DLR-RM/stable-baselines3)
• Model-Based and Hybrid Methods

  • Model-Based Policy Optimization (MBPO) – Leverages learned dynamics models

  • Guided Policy Search – Uses trajectory optimization to supervise policy learning

  • Survey: “Reinforcement Learning in Robotic Applications” (https://doi.org/10.1007/s10462-021-09997-9)
• Multi-Agent and Hierarchical RL

  • Multi-Agent Deep Q-Learning (MADDPG) – Cooperative and competitive settings

  • Hierarchical RL (options framework) – Temporal abstractions for long-horizon tasks

Robotics Applications

• Locomotion & Legged Control

  • Learning stable walking, running gaits on quadrupeds and bipeds

  • NVIDIA’s Legged Gym environments (https://developer.nvidia.com/isaac-legged-gym)
• Manipulation & Grasping

  • End-to-end policies for pick-and-place, tool use, and dexterous in-hand manipulation

  • Dex-Net grasp planner with RL integration (https://berkeleyautomation.github.io/dex-net)
• Navigation & Mobile Robotics

  • Maze solving, obstacle avoidance, and mapless navigation with deep RL

  • ROS-Gazebo RL tutorials (http://wiki.ros.org/gym_gazebo)
• Sim-to-Real Transfer

  • Domain Randomization and Sim-to-Real pipelines in NVIDIA Isaac Sim (https://developer.nvidia.com/isaac-sim)
• Aerial Robotics

  • Autonomous flight control for drones via RL

  • Microsoft AirSim environments (https://github.com/microsoft/AirSim)

Software Frameworks & Toolkits

• OpenAI Gym & Gym-Robotics (https://gym.openai.com/envs/#robotics)
• ROS RL Packages & ROS-Gym Bridges (https://github.com/ros-gym)
• NVIDIA Isaac RL & Isaac Gym (https://developer.nvidia.com/isaac-gym)
• Ray RLlib: Scalable RL library (https://docs.ray.io/en/latest/rllib.html)
• Unity ML-Agents: Game-engine–based RL (https://github.com/Unity-Technologies/ml-agents)
• Intel Coach: Research RL framework (https://github.com/intel/coach)

Online Courses & Tutorials

• Coursera “Reinforcement Learning Specialization” by University of Alberta (https://www.coursera.org/specializations/reinforcement-learning)
• Udacity “Deep Reinforcement Learning Nanodegree” (https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893)
• The Construct Academy “Reinforcement Learning for Robotics” (https://www.theconstruct.ai/robotigniteacademy_learnros/ros-courses-library/reinforcement-learning-for-robotics/)
• 30 Days Coding “RL for Robotics: Locomotion & Navigation” (https://30dayscoding.com/blog/reinforcement-learning-for-robotics-locomotion-and-navigation)

Key Survey Papers

• Kober, Bagnell & Peters (2013), “Reinforcement Learning in Robotics: A Survey” (https://www.ias.informatik.tu-darmstadt.de/uploads/Publications/Kober_IJRR_2013.pdf)
• Deisenroth, Neumann & Peters (2011), “A Survey on Policy Search for Robotics” (https://spiral.imperial.ac.uk/bitstream/10044/1/12051/7/fnt_corrected_2014-8-22.pdf)
• Singh et al. (2021), “Reinforcement Learning in Robotic Applications: A Comprehensive Survey” (https://doi.org/10.1007/s10462-021-09997-9)

By blending these algorithms, platforms, and learning pathways, practitioners can accelerate the deployment of RL-powered robots-from simulated prototypes to real-world autonomy.