Library
Our platform provides a Skill Library that breaks down real-world tasks into modular components.
Multi-Task & Transfer Learning
Multi-task learning allows an agent to learn multiple skills simultaneously, sharing knowledge across tasks. Transfer learning leverages knowledge gained from one task (e.g., walking) to facilitate learning a related but different task (e.g., running). This synergy:
Boosts Efficiency: Each new skill can be acquired faster and with fewer data samples.
Enhances Generalization: Agents learn to adapt to varied scenarios, becoming more robust in real-world applications.
Here are the initial skills library:
Crawling: Basic locomotion in environments where constraints require the agent to navigate using minimal joints.
Standing: Learning to stabilize a body under gravity and external disturbances.
Speaking: Vocal or textual communication, trained using natural language processing modules integrated with RL strategies.
Walking: From bipedal to quadrupedal motions, refined with reward functions measuring smoothness and efficiency.
Running: Similar to walking but at higher speeds, requiring advanced control over momentum.
Skating: Novel locomotive dynamics that involve smooth gliding motions on a surface.
Fighting: More complex, multi-joint coordination for simulated martial arts-like interactions.
Dating (Conversational): Social interaction tasks, focusing on empathy, conversation flow, and context-awareness for more advanced human-agent communications.
Breeding: Two agents may breed after dating for a while and produce a child agent.
(And More): The Skill Library is constantly expanding and is driven by community input and research advancements.
Multi-Task & Transfer Learning
Multi-task learning allows an agent to learn multiple skills simultaneously, sharing knowledge across tasks. Transfer learning leverages knowledge gained from one task (e.g., walking) to facilitate learning a related but different task (e.g., running). This synergy:
Boosts Efficiency: Each new skill can be acquired faster and with fewer data samples.
Enhances Generalization: Agents learn to adapt to varied scenarios, becoming more robust in real-world applications.
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