[Paper Review] Enhanced Experience Replay Generation for Efficient Reinforcement Learning

Vincent Huang, Tobias Ley+2 more|arXiv (Cornell University)|May 23, 2017

Reinforcement Learning in Robotics8 references3 citations

TL;DR

This paper proposes Enhanced GAN-based experience replay (EGAN) to accelerate reinforcement learning in real-time systems with sparse and slow data sampling. By leveraging state-action-reward relationships to improve synthetic data quality, EGAN pre-trains the RL agent, achieving a 20% reduction in training time compared to no pre-training and a 5% improvement over standard GAN pre-training, with lower variance in performance.

ABSTRACT

Applying deep reinforcement learning (RL) on real systems suffers from slow data sampling. We propose an enhanced generative adversarial network (EGAN) to initialize an RL agent in order to achieve faster learning. The EGAN utilizes the relation between states and actions to enhance the quality of data samples generated by a GAN. Pre-training the agent with the EGAN shows a steeper learning curve with a 20% improvement of training time in the beginning of learning, compared to no pre-training, and an improvement compared to training with GAN by about 5% with smaller variations. For real time systems with sparse and slow data sampling the EGAN could be used to speed up the early phases of the training process.

Motivation & Objective

• Address the challenge of slow and sparse data sampling in real-time reinforcement learning (RL) environments, particularly in 5G telecom systems.
• Reduce the long training duration typical in real-world RL applications where exploration risks service quality.
• Improve sample efficiency by pre-training the RL agent using synthetic experience generated from a generative model.
• Enhance the quality of generated experience by modeling the relationship between states, actions, and rewards, beyond standard GANs.
• Develop a robust, efficient pre-training framework that enables faster convergence and stable learning in production-critical systems.

Proposed method

• Collect a small dataset of real experiences (500 episodes) using a random policy in the target environment.
• Train a Generative Adversarial Network (GAN) on the real experience data to generate synthetic state-action-reward tuples.
• Introduce an Enhancer neural network that models the conditional relation between states and actions to refine the quality of generated samples.
• Use the enhanced synthetic data (6000 batches) to pre-train the policy network of the RL agent before online training.
• Apply Proximal Policy Optimization (PPO) for online training, with the pre-trained policy as initialization.
• Compare training curves using 100-episode rolling average rewards, evaluating performance against no pre-training and standard GAN pre-training.

Experimental results

Research questions

• RQ1 Can pre-training an RL agent with synthetic experience generated by a GAN significantly reduce the time to reach optimal performance in real-world environments with sparse data?
• RQ2 How does modeling the state-action-reward relationship in the generative process affect the quality and utility of synthetic experience for RL pre-training?
• RQ3 Does the proposed EGAN framework improve sample efficiency and training stability compared to standard GAN-based pre-training and no pre-training?
• RQ4 What is the optimal pre-training length (in episodes) for balancing cost and performance gain in real production systems?
• RQ5 To what extent does EGAN reduce variance in learning curves, indicating improved robustness of the RL agent?

Key findings

• EGAN pre-training reduces the required training time by 20% compared to training without any pre-training, demonstrating significant sample efficiency gains.
• The EGAN approach achieves a 5% improvement in learning speed over standard GAN-based pre-training, indicating that the enhancer component effectively improves data quality.
• The learning curve of the EGAN-pretrained agent shows a steeper initial ascent, indicating faster policy optimization due to better initialization.
• The system exhibits lower variance in performance, as evidenced by tighter standard deviation bands in the rolling average reward curves, indicating greater robustness.
• Pre-training with 500 episodes of real data is more cost-effective and efficient than longer pre-training (e.g., 5000 episodes), as it achieves strong performance gains without excessive data overhead.
• The enhanced synthetic data from EGAN enables the RL agent to achieve higher mean rewards with fewer cumulative samples, confirming improved sample efficiency in sparse environments.