Using Natural Language for Reward Shaping in Reinforcement Learning (1903.02020v2)

Abstract: Recent reinforcement learning (RL) approaches have shown strong performance in complex domains such as Atari games, but are often highly sample inefficient. A common approach to reduce interaction time with the environment is to use reward shaping, which involves carefully designing reward functions that provide the agent intermediate rewards for progress towards the goal. However, designing appropriate shaping rewards is known to be difficult as well as time-consuming. In this work, we address this problem by using natural language instructions to perform reward shaping. We propose the LanguagE-Action Reward Network (LEARN), a framework that maps free-form natural language instructions to intermediate rewards based on actions taken by the agent. These intermediate language-based rewards can seamlessly be integrated into any standard reinforcement learning algorithm. We experiment with Montezuma's Revenge from the Atari Learning Environment, a popular benchmark in RL. Our experiments on a diverse set of 15 tasks demonstrate that, for the same number of interactions with the environment, language-based rewards lead to successful completion of the task 60% more often on average, compared to learning without language.

Using Natural Language for Reward Shaping in Reinforcement Learning: A Critical Review

The paper "Using Natural Language for Reward Shaping in Reinforcement Learning" presents a framework for enhancing reinforcement learning (RL) training efficiency through language-based reward shaping. The proposed system, termed the LanguagE-Action Reward Network (LEARN), leverages natural language instructions to generate intermediate rewards for RL agents, potentially making learning more sample efficient in environments with sparse extrinsic rewards.

Objective and Approach

The primary challenge addressed by the authors is the difficulty and resource-intensive nature of designing effective reward functions in RL environments. Sparse rewards often lead to prolonged exploration times and inefficient learning phases. By contrast, dense reward structures, though more effective for training, are harder to specify especially for complex tasks.

In response to these issues, LEARN is introduced as a framework that translates free-form natural language instructions into intermediate rewards. This reward shaping is designed to seamlessly integrate with existing RL algorithms, thereby enhancing learning efficiency without altering the foundational learning mechanisms. The authors experiment with Montezuma's Revenge from the Atari Learning Environment, a notoriously challenging RL benchmark, demonstrating improvements in task completion rates when utilizing language-based rewards.

Technical Details

The framework comprises two phases:

1. LanguagE-Action Reward Network (LEARN): This phase involves training a neural network to predict whether an agent's actions align with provided language instructions. The neural network utilizes action-frequency vectors derived from agent trajectories and language representations, trained on labeled data containing trajectory-language pairs.
2. Language-aided Reinforcement Learning: Once trained, LEARN informs the RL process by providing intermediate rewards based on its language-action predictions. The potential function derived from LEARN's predictions is employed to calculate these shaping rewards.

Experimental Evaluation

Testing on 15 tasks within the Montezuma's Revenge environment revealed promising results. Language-based reward shaping led to an average 60% increase in task completion for a given number of interactions with the environment compared to RL without language intervention. Statistical significance tests indicate marked improvements in learning efficiency across several tasks, reinforcing the utility of natural language in guiding RL exploration.

Analysis and Limitations

The paper analyses correlation between language-based rewards and specific action selections, finding that correct action correlations are often observed. However, some limitations include imperfect language-action groundings and occasional performance degradations in certain tasks, notably when vague instructions are involved. The authors speculate that these issues may arise from ambiguities inherent in natural language or from challenges in symbol grounding.

Implications and Future Directions

The integration of natural language into RL could potentially democratize task specification, allowing non-experts to effectively communicate objectives to RL agents using intuitive instructions. The framework could be adapted to various applications in AI-driven robotics, autonomous systems, and natural language interfaces.

Future work could enhance language-to-reward mappings by incorporating state-based signals alongside action sequences, thereby refining reward accuracy. Additionally, adaptations to handle multi-step instructions or temporal dependencies could further optimize learning processes.

Ultimately, the development of language-grounded RL systems opens avenues for more intuitive AI-human collaborations, emphasizing the importance of continued research into natural language understanding and symbol grounding within RL contexts.