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Reinforcement Learning in Robotics
Research Guide
What is Reinforcement Learning in Robotics?
Reinforcement Learning in Robotics is the application of reinforcement learning algorithms, where agents learn optimal control policies through trial-and-error interactions to maximize rewards, enabling robotic systems to perform tasks in dynamic physical environments such as locomotion, manipulation, and autonomous navigation.
The field encompasses 49,848 works focused on advancements in reinforcement learning algorithms and their integration with robotics, including deep learning, policy gradient methods, and simulation-to-real-world transfer. Key challenges addressed include the sim-to-real gap, where minor simulation discrepancies reduce controller effectiveness in physical robots, as shown in 'Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper)' (2017). Continuous control tasks in robotics benefit from actor-critic methods like those in 'Continuous control with deep reinforcement learning' (2016), which extend deep Q-learning to continuous action spaces using deterministic policy gradients.
Topic Hierarchy
Research Sub-Topics
Policy Gradient Methods in Reinforcement Learning
This sub-topic advances REINFORCE, PPO, and TRPO algorithms for continuous and high-dimensional action spaces in robotics control. Researchers analyze variance reduction, sample efficiency, and convergence guarantees.
Model-Based Reinforcement Learning
Studies develop world models, MBPO, and SLAC for planning and data-efficient learning in simulated robotics environments. Research evaluates sim-to-real transfer and uncertainty quantification in dynamic systems.
Multi-Agent Reinforcement Learning
This sub-topic explores cooperative and competitive MARL frameworks like QMIX and MADDPG for robotic swarms and human-robot teams. Researchers address non-stationarity, communication, and emergent behaviors.
Curiosity-Driven Exploration in RL
Researchers design intrinsic rewards via prediction errors (e.g., ICM, RND) to promote exploration in sparse-reward robotic tasks. Studies benchmark in locomotion and manipulation domains for long-horizon learning.
Sim-to-Real Transfer in Robotic RL
This sub-topic tackles domain randomization, system identification, and fine-tuning for transferring RL policies from simulation to hardware. Research optimizes for legged robots, drones, and manipulators amid reality gaps.
Why It Matters
Reinforcement Learning in Robotics enables development of controllers in simulation for safety and efficiency, addressing the sim-to-real gap that impacts real-world performance, as diagnosed in 'Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper)' by Schulman et al. (2017) with 11,204 citations. In continuous control applications like robotic locomotion and manipulation, 'Continuous control with deep reinforcement learning' by Lillicrap et al. (2016) demonstrates an actor-critic algorithm that learns effective policies over continuous action spaces using the same network architecture as deep Q-learning, achieving robust performance transferable from simulation to physical systems. These methods support autonomous control in uncertain environments, with foundational surveys like 'Reinforcement Learning: A Survey' by Kaelbling et al. (1996) highlighting their role in sequential decision-making for robotics.
Reading Guide
Where to Start
'Reinforcement Learning: An Introduction' (2005) provides the foundational computational framework of agents maximizing rewards in uncertain environments, making it the ideal starting point before robotics-specific applications.
Key Papers Explained
'Reinforcement Learning: An Introduction' (2005) establishes core concepts, which 'Reinforcement Learning: A Survey' by Kaelbling et al. (1996) expands with a computer-science perspective on current work including robotics. 'Continuous control with deep reinforcement learning' by Lillicrap et al. (2015 and 2016) builds on these by adapting deep Q-learning via deterministic policy gradients for continuous robotic actions. 'Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper)' by Schulman et al. (2017) addresses practical sim-to-real challenges in these policies.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Recent focus remains on sim-to-real transfer and continuous control, as no new preprints are available; frontiers involve scaling actor-critic methods and anomaly diagnosis from 'Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper)' (2017) to multi-agent robotic systems.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | Reinforcement Learning: An Introduction | 2005 | IEEE Transactions on N... | 25.7K | ✕ |
| 2 | Deep learning in neural networks: An overview | 2014 | Neural Networks | 17.6K | ✓ |
| 3 | Diagnosing Non-Intermittent Anomalies in Reinforcement Learnin... | 2017 | arXiv (Cornell Univers... | 11.2K | ✓ |
| 4 | Mastering the game of Go without human knowledge | 2017 | Nature | 8.9K | ✕ |
| 5 | Q-learning | 1992 | Machine Learning | 8.8K | ✓ |
| 6 | Reinforcement Learning: A Survey | 1996 | Journal of Artificial ... | 8.6K | ✓ |
| 7 | Markov Decision Processes: Discrete Stochastic Dynamic Program... | 1995 | Journal of the America... | 8.4K | ✕ |
| 8 | Continuous control with deep reinforcement learning | 2016 | arXiv (Cornell Univers... | 6.8K | ✓ |
| 9 | Finite-time Analysis of the Multiarmed Bandit Problem | 2002 | Machine Learning | 5.7K | ✕ |
| 10 | Continuous control with deep reinforcement learning | 2015 | arXiv (Cornell Univers... | 5.4K | ✓ |
Frequently Asked Questions
What is the sim-to-real gap in Reinforcement Learning in Robotics?
The sim-to-real gap arises from minor differences between simulation and the real world that reduce the effectiveness of reinforcement learning controllers developed in simulation. 'Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper)' (2017) identifies non-intermittent anomalies in policy executions as a key cause. This gap poses safety risks and training inefficiencies, making diagnosis essential for real-world deployment.
How does deep reinforcement learning handle continuous control in robotics?
Deep reinforcement learning adapts deep Q-learning to continuous action domains using actor-critic, model-free algorithms based on deterministic policy gradients. 'Continuous control with deep reinforcement learning' (2016) by Lillicrap et al. presents this approach, operating over continuous action spaces with shared network architectures and hyperparameters. The method enables learning of effective policies for robotic tasks like locomotion.
What are the core principles of reinforcement learning for robotics?
Reinforcement learning involves agents maximizing total reward through interactions with complex, uncertain environments. 'Reinforcement Learning: An Introduction' (2005) defines it as a computational approach central to artificial intelligence applications in robotics. Surveys like 'Reinforcement Learning: A Survey' (1996) by Kaelbling et al. summarize its basis in Markov decision processes for sequential decision-making.
Why use simulation for training robotic reinforcement learning policies?
Simulation allows safe and sample-efficient development of controllers due to risks in real-world training. 'Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper)' (2017) notes that despite this preference, sim-to-real discrepancies must be addressed. Methods in 'Continuous control with deep reinforcement learning' (2016) facilitate effective transfer to physical robots.
What role do policy gradients play in robotic reinforcement learning?
Policy gradient methods optimize continuous policies directly, suitable for high-dimensional robotic control spaces. 'Continuous control with deep reinforcement learning' (2015 and 2016) by Lillicrap et al. base their actor-critic algorithm on deterministic policy gradients. These extend discrete Q-learning successes to robotics applications.
Open Research Questions
- ? How can non-intermittent anomalies in reinforcement learning policies be automatically diagnosed and mitigated for reliable sim-to-real transfer in robotics?
- ? What network architectures and hyperparameters optimize deep reinforcement learning for continuous robotic control tasks beyond simulated environments?
- ? How do model discrepancies between simulation and reality affect policy robustness, and what diagnostics improve transfer?
- ? In what ways can actor-critic methods scale to multi-joint robotic systems with high-dimensional continuous action spaces?
- ? How might curiosity-driven exploration enhance sample efficiency in real-world robotic reinforcement learning?
Recent Trends
The field maintains 49,848 works with sustained interest in sim-to-real transfer, as evidenced by high citations of 'Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper)' (2017, 11,204 citations) and 'Continuous control with deep reinforcement learning' (2016, 6,769 citations; 2015, 5,352 citations), but no preprints or news in the last 12 months indicate steady rather than accelerating growth.
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