We lifted 4 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| albertometelli/pfqi | canonical | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| bound | Ran | albertometelli/pfqi/trlib/environments/acrobot_multitask.py code served (permissive licence) · get_code("dea6e2e4ceb554b6") |
| save_json_callback | Ran | albertometelli/pfqi/trlib/algorithms/callbacks.py code served (permissive licence) · get_code("83c342e0686d5882") |
| wrap | Ran | albertometelli/pfqi/trlib/environments/acrobot_multitask.py code served (permissive licence) · get_code("655d31ff95e8128e") |
| rk4 | Not yet run | albertometelli/pfqi/trlib/environments/acrobot_multitask.py code served (permissive licence) · get_code("f484ea31b437dd66") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The choice of the control frequency of a system has a relevant impact on the ability of reinforcement learning algorithms to learn a highly performing policy. In this paper, we introduce the notion of action persistence that consists in the repetition of an action for a fixed number of decision steps, having the effect of modifying the control frequency. We start analyzing how action persistence affects the performance of the optimal policy, and then we present a novel algorithm, Persistent Fitted Q-Iteration (PFQI), that extends FQI, with the goal of learning the optimal value function at a given persistence. After having provided a theoretical study of PFQI and a heuristic approach to identify the optimal persistence, we present an experimental campaign on benchmark domains to show the advantages of action persistence and proving the effectiveness of our persistence selection method.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2002.06836")
get_code_for_paper("2002.06836")
have("2002.06836")
Connect an agent — have() is free.