A Preprint, Florian Fletzer, Daniel Majoral, Raul Vicente
We lifted 7 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| aqeel13932/mn_project | canonical | 6 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| Construct_Scenario | Ran | aqeel13932/mn_project/Scenarios.py pointer only (licence: NONE) · get_code("1c4bac912b603373") |
| Get_LSTM_Files | Ran | aqeel13932/mn_project/miscellaneous.py pointer only (licence: NONE) · get_code("222ea5c3fe474a08") |
| Jetlag_exp_generator | Ran | aqeel13932/mn_project/Scenarios.py pointer only (licence: NONE) · get_code("3bb1fa0058d72e8d") |
| calculate_mean_window | Ran | aqeel13932/mn_project/miscellaneous.py pointer only (licence: NONE) · get_code("e27d14055f48c6c0") |
| trace_tracker | Ran | aqeel13932/mn_project/bif_mn_record.py pointer only (licence: NONE) · get_code("8e3f2465401d5501") |
| trace_tracker | Ran | aqeel13932/mn_project/mn_record.py pointer only (licence: NONE) · get_code("dcaf37897ed69139") |
| All_in_one | Not yet run | aqeel13932/mn_project/miscellaneous.py pointer only (licence: NONE) · get_code("8fbac4eb78a2a8a4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Adapting to regularities of the environment is critical for biological organisms to anticipate events and plan. A prominent example is the circadian rhythm corresponding to the internalization by organisms of the 24-hour period of the Earth's rotation. In this work, we study the emergence of circadian-like rhythms in deep reinforcement learning agents. In particular, we deployed agents in an environment with a reliable periodic variation while solving a foraging task. We systematically characterize the agent's behavior during learning and demonstrate the emergence of a rhythm that is endogenous and entrainable. Interestingly, the internal rhythm adapts to shifts in the phase of the environmental signal without any re-training. Furthermore, we show via bifurcation and phase response curve analyses how artificial neurons develop dynamics to support the internalization of the environmental rhythm. From a dynamical systems view, we demonstrate that the adaptation proceeds by the emergence of a stable periodic orbit in the neuron dynamics with a phase response that allows an optimal phase synchronisation between the agent's dynamics and the environmental rhythm.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2307.12143")
get_code_for_paper("2307.12143")
have("2307.12143")
Connect an agent — have() is free.