Aligning Actual Returns with Target Returns in Offline Reinforcement Learning using Return-Aligned Decision Transformer

Core Concepts

The proposed Return-Aligned Decision Transformer (RADT) model effectively aligns the actual return obtained by the agent with a specified target return, enabling control over the agent's performance.

Abstract

The paper introduces the Return-Aligned Decision Transformer (RADT), a novel architecture designed to address the discrepancy between the actual return and the target return observed in existing Decision Transformer (DT) models.
Key highlights:

Traditional offline reinforcement learning methods aim to maximize the cumulative reward (return), but in many applications, it is crucial to train agents that can align the actual return with a specified target return.
DT, a recent approach that conditions action generation on the target return, exhibits discrepancies between the actual and target returns.
RADT decouples the returns from the input sequence of states and actions, and employs two key techniques to explicitly model the relationships between returns and other modalities:

A unique cross-attention mechanism that focuses on the relationship between the state-action sequence and the return sequence.
Adaptive layer normalization, which scales the state-action features using parameters inferred from the return features.

Extensive experiments show that RADT significantly reduces the discrepancies between the actual and target returns compared to DT and other baselines, in both continuous control (MuJoCo) and discrete control (Atari) domains.
An ablation study demonstrates the effectiveness of the individual techniques and their complementary nature when used together.
RADT also achieves competitive or superior performance compared to baselines in the standard task of maximizing the expected return.

Stats

The actual return obtained by the agent is lower than the specified target return.
The absolute error between the actual return and the target return is reduced by 39.7% in the MuJoCo domain and 29.8% in the Atari domain compared to the Decision Transformer baseline.

Quotes

"Despite being designed to align the actual return with the target return, we have empirically identified a discrepancy between the actual return and the target return in DT."
"Our model decouples returns from the conventional input sequence, which typically consists of returns, states, and actions, to enhance the relationships between returns and states, as well as returns and actions."

Key Insights Distilled From

Return-Aligned Decision Transformer

by Tsunehiko Ta... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2402.03923.pdf

Deeper Inquiries

How can the proposed techniques in RADT be extended to other reinforcement learning settings, such as online or multi-agent environments

The techniques proposed in RADT can be extended to other reinforcement learning settings by adapting them to suit the specific requirements of different environments. For online reinforcement learning, where agents interact with the environment in real-time, the cross-attention mechanism in RADT can be modified to incorporate feedback from the environment during training. This adaptation would allow the agent to adjust its actions based on the actual outcomes experienced during online interactions. Additionally, in multi-agent environments, the adaptive layer normalization in RADT can be enhanced to consider the actions and states of other agents, enabling better coordination and alignment of returns among multiple agents. By incorporating these modifications, RADT can be applied effectively in a variety of reinforcement learning settings beyond offline RL.

What are the potential limitations of the return-conditioned approach, and how can they be addressed in future research

One potential limitation of the return-conditioned approach is the reliance on accurate target returns for training. In real-world scenarios, defining precise target returns may be challenging, leading to discrepancies between the actual and target returns. To address this limitation, future research could explore the use of reward shaping techniques to provide more informative target returns. By shaping the rewards to guide the agent towards desired behaviors, the target returns can be better aligned with the actual returns, improving the overall performance of the agent. Additionally, incorporating uncertainty estimation methods into the return-conditioned approach can help account for the variability in target returns, making the agent more robust to inaccuracies in the target return specification.

What insights from human decision-making processes could be leveraged to further improve the alignment between actual and target returns in reinforcement learning agents

Insights from human decision-making processes, such as cognitive biases and heuristics, can be leveraged to improve the alignment between actual and target returns in reinforcement learning agents. For example, incorporating principles from behavioral economics, like loss aversion and prospect theory, can help agents better evaluate the consequences of their actions and adjust their behavior accordingly. By integrating these human-inspired decision-making strategies into the training process, agents can learn to make decisions that are more in line with the specified target returns. Furthermore, techniques from explainable AI can be utilized to provide agents with interpretable feedback on their decisions, enabling them to understand and adapt to the target returns more effectively. By drawing on insights from human decision-making, reinforcement learning agents can enhance their ability to align actual returns with target returns and improve their overall performance.