Pulkit Agrawal, Joni Pajarinen, Eric Chen, Zhang-Wei Hong
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 |
|---|---|---|
| Improbable-AI/eipo | canonical | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| conv2d_output_shape | Ran | Improbable-AI/eipo/rlpyt/models/utils.py code served (permissive licence) · get_code("fc2d9d7f350a539e") |
| strip_ddp_state_dict | Ran | Improbable-AI/eipo/rlpyt/models/utils.py code served (permissive licence) · get_code("6b1cccb4333e7320") |
| wrap_input | Ran | Improbable-AI/eipo/utilities/text_utils.py code served (permissive licence) · get_code("dbd699e563cfcdf4") |
| row_matrix | Not yet run | Improbable-AI/eipo/utilities/text_utils.py code served (permissive licence) · get_code("6eaec1d72033887f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
State-of-the-art reinforcement learning (RL) algorithms typically use random sampling (e.g., -greedy) for exploration, but this method fails on hard exploration tasks like Montezuma's Revenge. To address the challenge of exploration, prior works incentivize exploration by rewarding the agent when it visits novel states. Such intrinsic rewards (also called exploration bonus or curiosity) often lead to excellent performance on hard exploration tasks. However, on easy exploration tasks, the agent gets distracted by intrinsic rewards and performs unnecessary exploration even when sufficient task (also called extrinsic) reward is available. Consequently, such an overly curious agent performs worse than an agent trained with only task reward. Such inconsistency in performance across tasks prevents the widespread use of intrinsic rewards with RL algorithms. We propose a principled constrained optimization procedure called Extrinsic-Intrinsic Policy Optimization (EIPO) that automatically tunes the importance of the intrinsic reward: it suppresses the intrinsic reward when exploration is unnecessary and increases it when exploration is required. The results is superior exploration that does not require manual tuning in balancing the intrinsic reward against the task reward. Consistent performance gains across sixty-one ATARI games validate our claim. The code is available at https://github.com/Improbable-AI/eipo.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2211.07627")
get_code_for_paper("2211.07627")
have("2211.07627")
Connect an agent — have() is free.