Saurabh Kumar, Sergey Levine, Chelsea Finn, Aviral Kumar
We lifted 7 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| egiob/diversityisallyouneed-sb3 | pwc_unofficial | 5 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| crawler_disc_on_y_legs | Ran | egiob/diversityisallyouneed-sb3/stable_baselines3/common/behaviour_functions.py code served (permissive licence) · get_code("9f397819ac46d7f7") |
| crawler_is_flying | Ran | egiob/diversityisallyouneed-sb3/stable_baselines3/common/behaviour_functions.py code served (permissive licence) · get_code("d832171813a56c1f") |
| crawler_sensor_outputs | Ran | egiob/diversityisallyouneed-sb3/stable_baselines3/common/behaviour_functions.py code served (permissive licence) · get_code("82e6460bcd964905") |
| create_mlp | Ran | egiob/diversityisallyouneed-sb3/stable_baselines3/common/torch_layers.py code served (permissive licence) · get_code("ef103f787c48beca") |
| sum_independent_dims | Ran | egiob/diversityisallyouneed-sb3/stable_baselines3/common/distributions.py code served (permissive licence) · get_code("ee85c29e20e06d17") |
| get_actor_critic_arch | Not yet run | egiob/diversityisallyouneed-sb3/stable_baselines3/common/torch_layers.py code served (permissive licence) · get_code("f2ded9c22d608f44") |
| kl_divergence | Not yet run | egiob/diversityisallyouneed-sb3/stable_baselines3/common/distributions.py code served (permissive licence) · get_code("df19469790ae30d9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
While reinforcement learning algorithms can learn effective policies for complex tasks, these policies are often brittle to even minor task variations, especially when variations are not explicitly provided during training. One natural approach to this problem is to train agents with manually specified variation in the training task or environment. However, this may be infeasible in practical situations, either because making perturbations is not possible, or because it is unclear how to choose suitable perturbation strategies without sacrificing performance. The key insight of this work is that learning diverse behaviors for accomplishing a task can directly lead to behavior that generalizes to varying environments, without needing to perform explicit perturbations during training. By identifying multiple solutions for the task in a single environment during training, our approach can generalize to new situations by abandoning solutions that are no longer effective and adopting those that are. We theoretically characterize a robustness set of environments that arises from our algorithm and empirically find that our diversity-driven approach can extrapolate to various changes in the environment and task.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.14484")
get_code_for_paper("2010.14484")
have("2010.14484")
Connect an agent — have() is free.