Efficient Diffusion Policies for Offline Reinforcement Learning (2305.20081v2)

Abstract: Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL significantly boosts the performance of offline RL by representing a policy with a diffusion model, whose success relies on a parametrized Markov Chain with hundreds of steps for sampling. However, Diffusion-QL suffers from two critical limitations. 1) It is computationally inefficient to forward and backward through the whole Markov chain during training. 2) It is incompatible with maximum likelihood-based RL algorithms (e.g., policy gradient methods) as the likelihood of diffusion models is intractable. Therefore, we propose efficient diffusion policy (EDP) to overcome these two challenges. EDP approximately constructs actions from corrupted ones at training to avoid running the sampling chain. We conduct extensive experiments on the D4RL benchmark. The results show that EDP can reduce the diffusion policy training time from 5 days to 5 hours on gym-locomotion tasks. Moreover, we show that EDP is compatible with various offline RL algorithms (TD3, CRR, and IQL) and achieves new state-of-the-art on D4RL by large margins over previous methods. Our code is available at https://github.com/sail-sg/edp.

Efficient Diffusion Policies for Offline Reinforcement Learning: A Technical Overview

The paper "Efficient Diffusion Policies for Offline Reinforcement Learning" addresses two significant challenges faced by the current state-of-the-art diffusion-based models in offline reinforcement learning (RL), specifically in Diffusion-QL. The authors propose Efficient Diffusion Policy (EDP), a method to mitigate computational inefficiencies and extend compatibility with maximum likelihood-based RL algorithms.

Core Issues and Solutions

Diffusion-QL has demonstrated substantial improvements in policy performance by representing policies using diffusion models. However, this approach is hampered by two primary drawbacks: exhaustive computational requirements due to the sampling process of long parameterized Markov chains and incompatibility with maximum likelihood-based RL algorithms, as computing the likelihood of diffusion models is typically intractable.

The proposed EDP method innovatively addresses these issues:

1. Computational Efficiency: EDP introduces the concept of approximating actions from corrupted variants during training, thereby eliminating the need for running the sampling chain fully. This results in a significant reduction of training time from 5 days to 5 hours on gym-locomotion tasks, as demonstrated by comprehensive experiments on the D4RL benchmark. The method leverages DPM-Solver, a faster ODE-based sampler to further expedite training and sampling processes.
2. Generality: The ability of diffusion models to parameterize policies makes them restricted to certain RL algorithms, particularly TD3-based approaches. EDP overcomes this by using the evidence lower bound for likelihood calculation and approximating policy likelihood, facilitating compatibility with diverse RL algorithms such as TD3, CRR, and IQL. These modifications enable EDP to achieve new state-of-the-art results on D4RL with substantial performance margins over previous methods.

Numerical and Experimental Insights

The experimental results underscore the efficiency and effectiveness of EDP in overcoming the limitations of existing diffusion policies. On gym-locomotion tasks of the D4RL benchmark, EDP achieves state-of-the-art performance with diffusion models trained using higher timesteps, providing evidence that EDP can train policy networks on a more fine-grained scale without performance loss. The authors also report a notable numerical improvement across various domains of D4RL, particularly in environments with diverse and complex data distributions.

Implications and Future Directions

The introduction of EDP has both practical and theoretical implications. Practically, it reduces computational overhead substantially, making diffusion-based policies more viable for real-world applications where offline data aggregation from diverse sources becomes necessary. Theoretically, it broadens the applicability of diffusion models within RL, paving the way for future research toward more efficient generative modeling in policy learning.

Future developments in AI and RL algorithms can explore similar approximative techniques to further enhance computational efficiency, particularly in complex environments involving high-dimensional action spaces. Additionally, expanding EDP to online or hybrid RL paradigms could be valuable in exploring how generative models can benefit broader decision-making processes and adaptive learning systems.

In summary, the Efficient Diffusion Policies (EDP) framework proposed in the paper provides an insightful advancement in offline RL, systematically addressing inherent inefficiencies and model constraints. It opens avenues for improved performance on a broader set of RL algorithms and tasks, setting a benchmark for future exploration and refinement in diffusion-based policy parameterization.