Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
126 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards (1707.08817v2)

Published 27 Jul 2017 in cs.AI

Abstract: We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards. We build upon the Deep Deterministic Policy Gradient (DDPG) algorithm to use demonstrations. Both demonstrations and actual interactions are used to fill a replay buffer and the sampling ratio between demonstrations and transitions is automatically tuned via a prioritized replay mechanism. Typically, carefully engineered shaping rewards are required to enable the agents to efficiently explore on high dimensional control problems such as robotics. They are also required for model-based acceleration methods relying on local solvers such as iLQG (e.g. Guided Policy Search and Normalized Advantage Function). The demonstrations replace the need for carefully engineered rewards, and reduce the exploration problem encountered by classical RL approaches in these domains. Demonstrations are collected by a robot kinesthetically force-controlled by a human demonstrator. Results on four simulated insertion tasks show that DDPG from demonstrations out-performs DDPG, and does not require engineered rewards. Finally, we demonstrate the method on a real robotics task consisting of inserting a clip (flexible object) into a rigid object.

Citations (637)

Summary

  • The paper introduces a demonstration-augmented DDPG method that significantly improves performance on robotics tasks with sparse rewards.
  • It employs a replay buffer preloading and prioritized sampling to balance expert demonstrations with agent experiences, enhancing learning efficiency.
  • Experimental results on simulated insertion tasks and a real Sawyer robotics task confirm that the approach eliminates the need for hand-engineered reward shaping.

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

The paper "Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards" presents a method for improving reinforcement learning (RL) in robotics, particularly addressing the challenges imposed by sparse rewards. This work leverages demonstrations, enhancing the traditional RL approaches in high-dimensional control tasks often encountered in robotics.

Key Contributions

The authors build upon the Deep Deterministic Policy Gradient (DDPG) algorithm, incorporating demonstrations to address the limitations posed by sparse rewards. They propose a model-free approach that utilizes a replay buffer populated by both demonstrated and interaction-derived transitions. A priority mechanism is employed to dynamically balance the sampling ratio between demonstrations and transitions, ensuring more effective learning.

Experimental Results

The research demonstrates strong results across four simulated robotic insertion tasks, showing that DDPG augmented with demonstrations (DDPGfD) surpasses standard DDPG implementations. Notably, the DDPGfD approach eliminates the necessity for hand-engineered shaping rewards, a typical requirement in classical RL that often demands substantial domain expertise.

One of the significant findings is that DDPGfD achieves robust training behavior and demonstrates superior performance in scenarios defined by sparse rewards. The experiment involving a real-world robotic task—insert a flexible clip into a rigid object using a Sawyer robotic arm—further confirms the viability of the approach in practical settings.

Technical Insights

The methodology introduced several strategic modifications to the DDPG algorithm, emphasizing:

  1. Replay Buffer Preloading: Demonstration transitions are loaded into the replay buffer prior to training, allowing the agent to bootstrap efficiently.
  2. Prioritized Replay: The algorithm assigns sampling priorities based on a calculated TD error, actor network loss gradients, and added constants for demonstration data to ensure efficient backward propagation of rewards.
  3. Return Propagation: A combination of 1-step and n-step returns enhances the spread of reward information across trajectories, crucial in sparse reward environments.
  4. Frequent Learning Updates: Increasing the number of learning updates per environment step improves data efficiency, counteracting potential learning instability.
  5. Regularization: L2 regularization stabilizes learning, applied to both actor and critic networks to prevent overfitting.

Implications and Future Directions

The integration of demonstration data addresses the deep exploration challenges present in tasks defined by sparse rewards, avoiding the pitfalls of reward-shaping errors. This work not only simplifies the design of reward functions but also makes RL more accessible to a broader range of robotics applications.

The success of DDPGfD suggests robust pathways for incorporating demonstration-based learning in other domains of AI and robotics. Future research could explore more diverse types of demonstrations, including those generated synthetically or through transfer learning. Additionally, extending this approach to more complex multi-stage tasks and dynamic environments could further validate its effectiveness.

This research underscores a promising direction for reinforcement learning, utilizing human demonstrations to enhance AI learning efficiency and performance, particularly in tasks traditionally daunting for standard RL methods.

Youtube Logo Streamline Icon: https://streamlinehq.com