On the model-based stochastic value gradient for continuous reinforcement learning
This work addresses the sample-efficiency and performance gap in continuous reinforcement learning for robotics and control applications, offering a novel approach that outperforms existing methods.
The paper tackled the problem of model-based reinforcement learning agents underperforming in final reward compared to model-free agents by proposing a simple model-based agent that combines a model-free soft value estimate for policy evaluation with a model-based stochastic value gradient for policy improvement, achieving state-of-the-art results on a high-dimensional humanoid control task.
For over a decade, model-based reinforcement learning has been seen as a way to leverage control-based domain knowledge to improve the sample-efficiency of reinforcement learning agents. While model-based agents are conceptually appealing, their policies tend to lag behind those of model-free agents in terms of final reward, especially in non-trivial environments. In response, researchers have proposed model-based agents with increasingly complex components, from ensembles of probabilistic dynamics models, to heuristics for mitigating model error. In a reversal of this trend, we show that simple model-based agents can be derived from existing ideas that not only match, but outperform state-of-the-art model-free agents in terms of both sample-efficiency and final reward. We find that a model-free soft value estimate for policy evaluation and a model-based stochastic value gradient for policy improvement is an effective combination, achieving state-of-the-art results on a high-dimensional humanoid control task, which most model-based agents are unable to solve. Our findings suggest that model-based policy evaluation deserves closer attention.