Antoine Cully, Luca Grillotti, Maxence Faldor, Borja González León
We lifted 3 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| adaptive-intelligent-robotics/QDAC | canonical | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| get_override_config | Ran | adaptive-intelligent-robotics/QDAC/main_qdac_mb.py code served (permissive licence) · get_code("88ea68f4b60a6c90") |
| get_act | Not yet run | adaptive-intelligent-robotics/QDAC/dreamerv3/nets.py code served (permissive licence) · get_code("03dd934385b4d240") |
| load_repertoire_ppga | Not yet run | adaptive-intelligent-robotics/QDAC/main_adaptation_wall.py code served (permissive licence) · get_code("bc0b8932775f840b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
A key aspect of intelligence is the ability to demonstrate a broad spectrum of behaviors for adapting to unexpected situations. Over the past decade, advancements in deep reinforcement learning have led to groundbreaking achievements to solve complex continuous control tasks. However, most approaches return only one solution specialized for a specific problem. We introduce Quality-Diversity Actor-Critic (QDAC), an offpolicy actor-critic deep reinforcement learning algorithm that leverages a value function critic and a successor features critic to learn high-performing and diverse behaviors. In this framework, the actor optimizes an objective that seamlessly unifies both critics using constrained optimization to (1) maximize return, while (2) executing diverse skills. Compared with other Quality-Diversity methods, QDAC achieves significantly higher performance and more diverse behaviors on six challenging continuous control locomotion tasks. We also demonstrate that we can harness the learned skills to adapt better than other baselines to five perturbed environments. Finally, qualitative analyses showcase a range of remarkable behaviors: adaptive-intelligent-robotics.github.io/QDAC.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2403.09930")
get_code_for_paper("2403.09930")
have("2403.09930")
Connect an agent — have() is free.