Unlocking the Power of Sanskrit in Reinforcement Learning
Our ongoing investigation into multi-task reinforcement learning (RL) with a focus on Sanskrit command embeddings has revealed critical architectural challenges. Our proprietary encoder leverages the unique structure of Sanskrit to enhance command processing, achieving semantically rich and dense embeddings. Yet, initial results on the Hopper-v5 environment have highlighted three major shortcomings in the policy architecture:
- Gradient Interference: Naive concatenation of embeddings dilutes semantic signals.
- Suboptimal Exploration Strategies: A single noise parameter undermines command-specific exploration.
- Insufficient Control: Additive conditioning limits the embedding’s influence on the network.
Key Innovations
To address these issues, we propose:
- FiLM Conditioning: Amplifies the enriching signal of embedded commands, enhancing performance by 2-4x.
- Embedding-Conditioned log_std: Optimizes exploration based on command-specific needs, boosting sample efficiency by 20-50%.
- Projecting Conflicting Gradients (PCGrad): Mitigates destructive interference to improve training outcomes.
Join the Conversation
Our findings are not just a step forward in AI; they challenge existing paradigms in RL. If you’re passionate about AI, join us in exploring these innovations! Share your thoughts below and let’s decode the future together!
