We lifted 10 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 |
|---|---|---|
| Hwhitetooth/lirpg | canonical | 6 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| make_output_format | Ran | Hwhitetooth/lirpg/baselines/logger.py pointer only (licence: MIT) · get_code("5eee517402b390d5") |
| profile | Ran | Hwhitetooth/lirpg/baselines/logger.py pointer only (licence: MIT) · get_code("98948fae5be7e92e") |
| rolling_window | Ran | Hwhitetooth/lirpg/baselines/results_plotter.py pointer only (licence: MIT) · get_code("895ca4b776fb6d6d") |
| safemean | Ran | Hwhitetooth/lirpg/baselines/a2c/a2c.py pointer only (licence: MIT) · get_code("a7b323241cd70612") |
| ts2xy | Ran | Hwhitetooth/lirpg/baselines/results_plotter.py pointer only (licence: MIT) · get_code("8c5b970d902942f8") |
| window_func | Ran | Hwhitetooth/lirpg/baselines/results_plotter.py pointer only (licence: MIT) · get_code("43741b9339bd80da") |
| cat_entropy | Not yet run | Hwhitetooth/lirpg/baselines/a2c/utils.py pointer only (licence: MIT) · get_code("a3b4038f4e9b9479") |
| cat_entropy_softmax | Not yet run | Hwhitetooth/lirpg/baselines/a2c/utils.py pointer only (licence: MIT) · get_code("7082b4bbc0d9384f") |
| read_json | Not yet run | Hwhitetooth/lirpg/baselines/logger.py pointer only (licence: MIT) · get_code("88ef5a17fc986d9a") |
| sample | Not yet run | Hwhitetooth/lirpg/baselines/a2c/utils.py pointer only (licence: MIT) · get_code("178099b1f8fb2b9d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In many sequential decision making tasks, it is challenging to design reward functions that help an RL agent efficiently learn behavior that is considered good by the agent designer. A number of different formulations of the reward-design problem, or close variants thereof, have been proposed in the literature. In this paper we build on the Optimal Rewards Framework of Singh et.al. that defines the optimal intrinsic reward function as one that when used by an RL agent achieves behavior that optimizes the task-specifying or extrinsic reward function. Previous work in this framework has shown how good intrinsic reward functions can be learned for lookahead search based planning agents. Whether it is possible to learn intrinsic reward functions for learning agents remains an open problem. In this paper we derive a novel algorithm for learning intrinsic rewards for policy-gradient based learning agents. We compare the performance of an augmented agent that uses our algorithm to provide additive intrinsic rewards to an A2C-based policy learner (for Atari games) and a PPO-based policy learner (for Mujoco domains) with a baseline agent that uses the same policy learners but with only extrinsic rewards. Our results show improved performance on most but not all of the domains.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1804.06459")
get_code_for_paper("1804.06459")
have("1804.06459")
Connect an agent — have() is free.