We lifted 1 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 |
|---|---|---|
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| create_param_tree | Ran | this paper's copy was not recorded; identical code first harvested from shyamsn97/hyper-nn pointer only · get_code("8143f1c0cbd2ab4e") |
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Goal-conditioned Reinforcement Learning (RL) aims at learning optimal policies, given goals encoded in special command inputs. Here we study goal-conditioned neural nets (NNs) that learn to generate deep NN policies in form of context-specific weight matrices, similar to Fast Weight Programmers and other methods from the 1990s. Using context commands of the form "generate a policy that achieves a desired expected return," our NN generators combine powerful exploration of parameter space with generalization across commands to iteratively find better and better policies. A form of weight-sharing HyperNetworks and policy embeddings scales our method to generate deep NNs. Experiments show how a single learned policy generator can produce policies that achieve any return seen during training. Finally, we evaluate our algorithm on a set of continuous control tasks where it exhibits competitive performance. Our code is public.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2207.01570")
get_code_for_paper("2207.01570")
have("2207.01570")
Connect an agent — have() is free.