Building open-ended learning agents involves challenges in pre-trained
language model (LLM) and reinforcement learning (RL) approaches. LLMs struggle
with context-specific real-time interactions, while RL methods face efficiency
issues for exploration. To this end, we propose OpenContra, a co-training
framework that cooperates LLMs and GRL to construct an open-ended agent capable
of comprehending arbitrary human instructions. The implementation comprises two
stages: (1) fine-tuning an LLM to translate human instructions into structured
goals, and curriculum training a goal-conditioned RL policy to execute
arbitrary goals; (2) collaborative training to make the LLM and RL policy learn
to adapt each, achieving open-endedness on instruction space. We conduct
experiments on Contra, a battle royale FPS game with a complex and vast goal
space. The results show that an agent trained with OpenContra comprehends
arbitrary human instructions and completes goals with a high completion ratio,
which proves that OpenContra may be the first practical solution for
constructing open-ended embodied agents.
Building Open-Ended Learning Agents: A Multi-Disciplinary Approach
In the field of artificial intelligence, building open-ended learning agents is a challenging task that requires expertise in pre-trained language models (LLMs) and reinforcement learning (RL) approaches. LLMs are powerful in understanding and generating human-like language, but struggle with real-time context-specific interactions. On the other hand, RL methods excel at decision-making in dynamic environments but face efficiency issues when it comes to exploration.
To tackle these challenges, the authors of this article propose a novel co-training framework called OpenContra. This framework aims to combine the strengths of LLMs and goal-conditioned RL (GRL) to construct an open-ended agent capable of comprehending arbitrary human instructions.
The Implementation of OpenContra
The implementation of OpenContra consists of two stages:
- Fine-tuning an LLM: The first step involves training an LLM to translate human instructions into structured goals. This allows the agent to understand and interpret the instructions provided by humans accurately.
- Curriculum training a goal-conditioned RL policy: In this stage, a goal-conditioned RL policy is trained to execute arbitrary goals. The RL policy learns how to make decisions and take actions to achieve these goals, effectively bridging the gap between understanding and execution.
Once these initial stages are completed, the authors introduce a collaborative training phase. This phase focuses on making the LLM and RL policy learn to adapt to each other, enabling the agent to achieve open-endedness in the instruction space. By iteratively training and refining both components, the agent becomes more proficient at understanding and executing various human instructions.
Experimental Results on Contra
To evaluate the effectiveness of OpenContra, the authors conducted experiments on Contra, a battle royale first-person shooter (FPS) game with a complex and vast goal space. The results of these experiments were highly promising.
The agent trained with OpenContra demonstrated the ability to comprehend arbitrary human instructions and achieved goals with a high completion ratio. This success highlights the practicality and capability of OpenContra in constructing open-ended embodied agents.
Multi-Disciplinary Nature of Open-Ended Learning Agents
The concepts explored in this article illustrate the multi-disciplinary nature of building open-ended learning agents. Pre-trained language models (LLMs), which are rooted in natural language processing (NLP), are combined with reinforcement learning (RL) techniques commonly used in robotics and gaming applications. This fusion of NLP and RL enables the agent to understand and execute human instructions in a dynamic and complex environment such as Contra.
Furthermore, the collaborative training approach adopted in OpenContra emphasizes the importance of combining different disciplines to enhance the capabilities of AI agents. By integrating the strengths of LLMs and RL, the agent becomes more versatile and adaptable, pushing the boundaries of what is possible in the field of open-ended AI.
Overall, the OpenContra framework presents a promising solution for constructing open-ended embodied agents. Its multi-disciplinary approach showcases the integration of pre-trained language models and reinforcement learning techniques, enabling the agent to comprehend and execute arbitrary human instructions. This research opens up new avenues for the development of AI agents with enhanced cognitive abilities and real-time decision-making capabilities.