Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation (AI summary)

Key Points

1. OpenContra is a co-training framework that combines large language models (LLMs) and goal-conditioned reinforcement learning (RL) to build an open-ended embodied learning agent capable of comprehending arbitrary human instructions.

2. LLMs struggle with context-specific real-time interactions, while RL methods face efficiency issues for exploration.

3. OpenContra conducts two stages of implementation: fine-tuning an LLM to translate human instructions into structured goals and training a goal-conditioned RL policy to execute arbitrary goals. It also collaboratively trains the LLM and RL policy to adapt to each other and complete goals corresponding to human instructions.

4. The challenge of building general-capable agents in the field of AI remains significant, and the research in open-ended learning is broadly categorized into pre-trained LLM agents for open-ended planning and RL-based methods for open-ended control.

5. OpenContra leverages a distributed RL framework with the Actor-League-Learner architecture to enhance training efficiency and incorporates a distributed approach to improve overall training efficiency. It also incorporates a distributed approach to ensure compatibility with network inputs.

6. The training inefficiency is mitigated through the iterative development of the game Contra by employing surgery to retain learned skills at the lowest training cost, enabling adaptation to a changing observation/goal space.

7. Evaluation results demonstrate that OpenContra achieves high completion ratios for goals corresponding to human instructions and excels over baselines, showcasing its practical potential for constructing open-ended embodied agents.

8. OpenContra exhibits the potential to build a practical solution for constructing open-ended embodied agents and showcases the deepened comprehension of the linguistic model regarding the environment achieved through collaborative training.

9. Despite positive results, the limitations of OpenContra include the need to research a truly open-ended goal description instead of the handcrafted goal space and support for multi-modality input/output to free from expensive feature engineering.

Summary

Proposed Co-Training Framework: OpenContra
The research paper proposes a co-training framework called OpenContra, which combines large language models (LLM) and goal-conditioned reinforcement learning (GRL) to create an open-ended embodied agent capable of understanding and executing arbitrary human instructions. The framework comprises two stages: independent training of LLM and RL agents to generate and execute goals, followed by collaborative training to adapt to each other and complete goals based on human instructions. The research uses a battle royale first-person shooter (FPS) game, Contra, as a testbed to validate the approach, including human-instructed tests to assess open-endedness.

Challenges Faced and Solution Introduction: OpenContra
The paper highlights the challenges faced by pre-trained language models (LLMs) and reinforcement learning (RL) methods in open-ended learning and introduces OpenContra as a solution. It emphasizes the independent training of LLM and RL agents, as well as the collaborative training to generate and execute goals corresponding to human instructions. The research includes detailed information on the implementation of OpenContra, including the fine-tuning of LLM for goal generation and the training of RL agents with a multi-step approach. Furthermore, the paper discusses the observation space, action space, and reward functions used in the context of the Contra game for evaluating the effectiveness of the proposed framework.

Empirical Evaluation of OpenContra
The empirical evaluation of OpenContra demonstrates its ability to comprehend arbitrary human instructions and complete goals with high accuracy. The paper presents a detailed analysis of the effectiveness of collaborative training, the performance of LLM-based goal generation, and the impact of various tuning methods on the overall training process. Additionally, it acknowledges the limitations of the current work and suggests future research directions, such as exploring truly open-ended goal description and supporting multi-modality input/output for the agent.

Conclusion: OpenContra Practical Solution
Overall, the paper presents OpenContra as a practical solution for constructing open-ended embodied agents and provides evidence of its potential effectiveness in the context of a battle royale FPS game.

Reference: https://arxiv.org/abs/2401.00006