DreamSmooth: Enhancing Model-Based Reinforcement Learning with Temporally Smoothed Rewards

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Nov 8 2023

In an article recently submitted to the ArXiV* server, researchers delved into Model-based Reinforcement Learning (MBRL). MBRL is renowned for its ability to acquire intricate behaviors efficiently through action planning and the generation of simulated trajectories based on reward predictions. Interestingly, the study reveals that reward prediction often poses challenges, especially for sparse and complex reward structures.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Inspired by the idea that humans can learn from approximate reward estimates, the researchers introduce an innovative approach called DreamSmooth. DreamSmooth focuses on predicting temporally smoothed rewards rather than exact rewards at specific time steps. Empirical results demonstrate that DreamSmooth outperforms existing methods in sample efficiency and final performance on long-horizon, sparse-reward tasks while maintaining performance on standard benchmarks like the Deepmind Control Suite and Atari.

Background

Human decision making often relies on approximate estimates of future rewards rather than precise rewards at each moment, as demonstrated by past studies. Approximate reward estimates are usually sufficient for learning tasks because predicting exact rewards can be challenging due to ambiguity, delays, or unobservability. This challenge is evident in various environments where states with and without rewards are indistinguishable.

In the domain of MBRL, the accuracy of reward models is crucial. Overestimating rewards may lead to suboptimal action choices, while underestimating them may cause an agent to disregard high-reward actions. However, despite its significance, the problem of reward prediction in MBRL has received limited attention.

Proposed Method

The approach to address the challenge of reward prediction in MBRL begins by providing a background on MBRL, offering a foundational understanding of the problem. The approach proceeds by exploring the practical challenges of predicting sparse reward signals. Finally, DreamSmooth, the proposed approach to enhance MBRL by mitigating the reward prediction problem, is introduced.

The background sets the stage by presenting the core elements of the problem, which revolves around the formulation of partially observable Markov decision processes (POMDPs) and the significance of reward models in MBRL. These reward models are pivotal in training agents to make informed decisions based on future rewards. This method also discusses the relevance of state-of-the-art MBRL algorithms like DreamerV3 and TD-MPC in this context.

Thus, this approach delves into the practical difficulties of reward prediction, which is often challenging in various environments. The authors emphasize that even advanced MBRL algorithms like DreamerV3 can struggle with predicting rewards accurately, mainly when rewards are sparse, ambiguous, or challenging to observe. Ambiguity, partial observability, and stochastic dynamics of environments further exacerbate the issue.

Finally, it underscores the critical nature of reward prediction in the context of policy learning. Poor reward prediction can lead to suboptimal policy outcomes, as demonstrated in scenarios where the reward model fails to detect specific reward completions and shows how inadequate reward prediction can be a performance bottleneck in MBRL.

Introducing DreamSmooth to solve these challenges simplifies reward prediction by enabling models to predict temporally smoothed rewards rather than exact rewards at each timestep. This novel approach enhances learning by reducing the strict requirements for predicting sparse rewards accurately. The authors provide insights into the implementation of DreamSmooth, demonstrating its simplicity and minimal computational overhead, making it a practical solution for MBRL algorithms.

Results

In the ablation studies, researchers examined several factors influencing the performance of DreamSmooth in the context of MBRL. Researchers investigated one potential issue concerning data imbalance attributed to the infrequency of sparse rewards. They conducted oversampling experiments, where they sampled sequences containing sparse rewards with a probability of p = 0.5.

This approach improved performance compared to the baseline. Still, it fell short of DreamSmooth, affirming that while data imbalance plays a role in challenging reward prediction, it is not the sole contributing factor. Additionally, oversampling requires domain knowledge about which rewards to prioritize, while DreamSmooth is versatile and adaptive to varying scales and frequencies of sparse rewards. Researchers explored another hypothesis regarding how the reward model size impacts performance.

To test this, they increased the size of the reward model by changing the number of layers and units. The results revealed that altering the reward model size without smoothing had negligible effects on performance, while DreamSmooth consistently outperformed all reward model sizes tested. The difficulty in reward prediction only partially results from model capacity issues.

Finally, the sensitivity of DreamSmooth to its smoothing parameters, σ for Gaussian smoothing and α for EMA, was assessed. The RoboDesk and Hand tasks experiments showed that DreamSmooth demonstrated robustness across a wide range of smoothing parameter values, with good performance maintained. It underlines the adaptability and resilience of DreamSmooth to variations in its parameters.

Conclusion

To sum up, this paper addresses the challenge of reward prediction in MBRL by introducing an effective solution called DreamSmooth. It exhibits exceptional performance in sparse reward tasks, particularly in scenarios with partial observability or stochastic environments.

Moreover, it delivers competitive results on well-established benchmarks such as DMC and Atari, underscoring its versatility across various tasks. It is worth noting that while DreamSmooth significantly improves reward prediction, its application may not consistently lead to enhanced task performance, as observed in Crafter. This discrepancy could arise from a potential shift towards prioritizing exploitation over exploration when predicting task rewards. Further exploration of this trade-off presents a promising avenue for future research.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.