SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning (2007.04938v4)

Abstract: Off-policy deep reinforcement learning (RL) has been successful in a range of challenging domains. However, standard off-policy RL algorithms can suffer from several issues, such as instability in Q-learning and balancing exploration and exploitation. To mitigate these issues, we present SUNRISE, a simple unified ensemble method, which is compatible with various off-policy RL algorithms. SUNRISE integrates two key ingredients: (a) ensemble-based weighted BeLLMan backups, which re-weight target Q-values based on uncertainty estimates from a Q-ensemble, and (b) an inference method that selects actions using the highest upper-confidence bounds for efficient exploration. By enforcing the diversity between agents using Bootstrap with random initialization, we show that these different ideas are largely orthogonal and can be fruitfully integrated, together further improving the performance of existing off-policy RL algorithms, such as Soft Actor-Critic and Rainbow DQN, for both continuous and discrete control tasks on both low-dimensional and high-dimensional environments. Our training code is available at https://github.com/pokaxpoka/sunrise.

• The paper presents SUNRISE, which mitigates error propagation in Bellman backups using ensemble-based weighting.
• It demonstrates that UCB-driven exploration effectively balances exploration and exploitation in off-policy deep RL.
• Empirical results show that SUNRISE outperforms baselines like SAC and Rainbow DQN in sample efficiency and stability.

SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

The paper "SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning" introduces a novel approach to improving the stability and performance of off-policy deep reinforcement learning (RL) by leveraging ensemble methods. The primary contribution of the paper is the SUNRISE framework, which integrates ensemble-based weighted BeLLMan backups and an exploration strategy based on upper-confidence bounds (UCB).

Problem Setting and Challenges

Off-policy RL, despite its success in a variety of domains such as video games and robotic control, often faces challenges related to sample inefficiency, instability in Q-learning, and the balance between exploration and exploitation. The paper identifies the propagation of errors in BeLLMan backups and inefficient exploration as key issues that limit the efficacy of existing off-policy algorithms like SAC and Rainbow DQN.

Proposed Solution: The SUNRISE Framework

The SUNRISE framework aims to address these challenges by using ensemble methods in two innovative ways:

1. Ensemble-Based Weighted BeLLMan Backups: The standard RL training process anticipates issues due to error propagation in BeLLMan updates. SUNRISE uses ensemble predictions to weigh BeLLMan backup contributions with confidence, effectively improving signal-to-noise ratios in training updates. This reweighting strategy allows the method to stabilize learning by down-weighting updates with high variance among ensemble estimates, which are typically associated with high target uncertainty.
2. UCB-Based Exploration: For effective exploration, SUNRISE employs an UCB-derived approach to action selection, borrowing ideas from multi-armed bandit strategies and adapting them for RL settings. It employs the mean and variance of Q-function predictions across ensembles to guide exploration by favoring actions with high upper confidence bounds, thereby balancing exploration with exploitation more effectively.

Empirical Evaluation and Results

Extensive experiments demonstrate the efficacy of SUNRISE across a broad spectrum of environments, including OpenAI Gym, DeepMind Control Suite, and Atari games. The experimental results illustrate that SUNRISE consistently enhances the performance of baseline algorithms (SAC and Rainbow DQN), achieving superior results in terms of sample efficiency and stability. Notably, in continuous control tasks, SUNRISE surpasses several state-of-the-art model-based methods such as PETS and POPLIN, while maintaining the computational efficiency inherent to model-free approaches.

Implications and Future Directions

The robust performance of SUNRISE highlights the potential of ensemble learning techniques in reinforcing stability and improving the exploratory capabilities of RL algorithms. Integrating ensemble methods into RL can address error propagation and enhance exploration without sacrificing computational efficiency.

The methodology opens avenues for future exploration in several directions:

• Adapting SUNRISE to other RL paradigms such as on-policy learning and offline RL, where ensemble methods could also manage variance and bias in policy evaluation.
• Enhancing scalability and reducing computational overhead through more sophisticated ensemble formation or parallelization strategies.
• Applying SUNRISE to real-world applications that require high robustness and reliability in decision-making, such as autonomous systems and large-scale robotics.

Overall, SUNRISE stands as a significant step towards resilient and efficient RL frameworks, providing a template for leveraging ensemble methods to resolve complex stability and exploration challenges in contemporary RL environments.