Language Assisted RL Agent

Problem Addressed

Traditional Reinforcement Learning (RL) agents excel at optimizing reward signals but lack the ability to interpret and act on high-level natural language instructions. This limits their adaptability in real-world scenarios where tasks are often defined through human commands. Our project bridges this gap by integrating natural language understanding (NLU) with RL, enabling agents to execute complex textual directives in structured environments.

Solution Developed

We designed a language-guided RL agent that translates textual instructions into actionable policies within a grid environment. Key components include:

Environment Design:
- A customizable grid world (built with Gymnasium) where the agent navigates to corners.
- Observation Space: Combines agent position (coordinates), BERT text embeddings of instructions, and optionally grid images.
- Reward Structure: Penalizes step count (-1 per step) and rewards goal completion (+60).
Language Integration:
- BERT Embeddings: Process textual instructions (e.g., “Go to the upper-right corner”) into semantic representations.
- Dual Fine-Tuning Strategies:
  - Classification Fine-Tuning: Trained BERT on a sequence classification task to cluster embeddings by goal labels, improving intra-class similarity.
  - Contrastive Learning: Enhanced distinction between ambiguous instructions (e.g., “top-left” vs. “bottom-left”) by pushing dissimilar commands apart in the embedding space.
RL Architecture:
- Proximal Policy Optimization (PPO) with two distinct architectures:
  - Multi-Input Network: Fuses agent coordinates and BERT text embeddings to guide policy decisions.
  - Visual Attention Pathway: Uses CNNs to process grid images and cross-attention mechanisms to align visual features with language embeddings, enabling spatial reasoning.

Technologies Used

Core Frameworks: Python, PyTorch, Gymnasium.
Language Models: BERT for text embeddings, fine-tuned with classification and contrastive learning.
RL Algorithms: Proximal Policy Optimization (PPO) with custom multi-input networks.

Future Work

Inverse Reinforcement Learning (IRL): Implement methods inspired by Inverse Reinforcement Learning with Natural Language Goals.
Multi-Goal Instructions: Expand the agent’s capability to handle complex instructions with multiple interdependent goals (e.g., “Collect the key, open the door, then reach the exit”).

Bibliography

🔗 Links