Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning (1803.00101v1)

Abstract: Recent model-free reinforcement learning algorithms have proposed incorporating learned dynamics models as a source of additional data with the intention of reducing sample complexity. Such methods hold the promise of incorporating imagined data coupled with a notion of model uncertainty to accelerate the learning of continuous control tasks. Unfortunately, they rely on heuristics that limit usage of the dynamics model. We present model-based value expansion, which controls for uncertainty in the model by only allowing imagination to fixed depth. By enabling wider use of learned dynamics models within a model-free reinforcement learning algorithm, we improve value estimation, which, in turn, reduces the sample complexity of learning.

• The paper presents a hybrid approach that integrates short-horizon simulated rollouts from a learned dynamics model with model-free Q-learning to reduce sample complexity.
• It formalizes the method within deterministic MDPs, using a trust horizon H to combine near-term rewards with conventional Q-value estimates.
• Empirical evaluations demonstrate significant improvements in learning efficiency and value estimation compared to baselines like DDPG in continuous state-action spaces.

Model-Based Value Expansion for Efficient Model-Free Reinforcement Learning

The paper introduces a novel approach termed Model-Based Value Expansion (MVE) to enhance the efficiency of model-free reinforcement learning (RL) algorithms by judiciously integrating model-based dynamics. This research is situated at the intersection of model-free and model-based RL, where the former offers expressive value function approximations but often demands considerable empirical data, while the latter showcases efficient learning but grapples with complex dynamical systems.

Core Contributions

MVE capitalizes on a learned dynamics model to simulate short-horizon rollouts, improving the quality of Q-value estimates in model-free methods like Q-learning. The core idea is to merge model-based and model-free value estimation by computing near-term predictions using the dynamics model while relying on traditional Q-value assessments for longer-term predictions. Notably, the authors address the challenge of model inaccuracy at longer horizons by restricting the dynamics model's use to a predetermined, trustworthy depth. This method circumvents reliance on complicated heuristics traditionally used to curtail model application in MF settings, thus reducing sample complexity.

Methodology

The authors formalize MVE within the framework of deterministic MDPs. They propose using a parameterized actor-critic model where the dynamics model performs simulated rollouts to depth $H$, regarded as trust depth, for dynamic state transitions and reward predictions. The estimated value is derived by summing short-term imagined returns with a projected value from the terminal state of the simulation. This hybrid value estimation introduces greater accuracy than a purely model-free approach, provided that the dynamics model reliably approximates the environment within the prescribed horizon.

Theoretical Insights

The paper presents a comprehensive analysis of the MSE reductions achieved through MVE, juxtaposing it against standalone model-free critics. Several conditions ensure this benefit: small model errors, appropriate choice of $H$, and the critic’s capacity to generalize from inside the imagined state distribution. The theoretical exploration reveals that while dynamics models enhance value estimation, excessive reliance could inversely affect performance due to potential misalignment between training and imagined state distributions. Thus, the authors suggest employing a distribution that approximates a fixed point of the policy-augmented dynamics function.

Experimental Evaluation

The empirical results validate MVE’s capacity to boost learning efficacy in environments modeled with continuous state-action spaces. Comparisons with baseline algorithms, including DDPG and model-based acceleration methods, highlight superior performance attributed to MVE, especially concerning sample efficiency and quality of value estimation. The experiments also stress the significance of the TD-$k$ trick relative to distribution matching, which tempers the distribution discrepancy between real and simulated experiences, mitigating potential instabilities in learning curves.

Potential Impact and Future Directions

The implications of MVE in RL systems are manifold. Practically, this methodology can facilitate the application of RL to real-world scenarios where data scarcity and dynamic complexity pose formidable challenges. Theoretically, MVE provides a promising direction for harmonizing model-free and model-based paradigms, paving the way for developing RL frameworks that are both computationally and sample efficient.

Future research could extend this work to probabilistic models and policies, exploring the interplay between generalized planning and exploration strategies powered by model-based insights. Additionally, refining dynamics models to dynamically adjust the trust horizon $H$ based on uncertainty quantification could yield further gains in RL performance.

In summary, the paper presents a well-grounded, theoretically rigorous strategy for advancing reinforcement learning by leveraging dynamics models smartly, offering a noteworthy stride towards AI systems that can learn efficiently from limited environmental interactions.