Tanmay Gangwani, Jian Peng, Yuan Zhou
We lifted 5 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| baicenxiao/shaping-advice | — | 5 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| Check_IN | Ran | baicenxiao/shaping-advice/maddpg/trainer/maddpg_spread.py pointer only (licence: NOASSERTION) · get_code("845912fb62faffa5") |
| In_P | Ran | baicenxiao/shaping-advice/maddpg/trainer/maddpg_spread.py pointer only (licence: NOASSERTION) · get_code("f1f0bbcd3b14929e") |
| SAM_NonUniform_b | Ran | baicenxiao/shaping-advice/maddpg/trainer/maddpg_spread.py pointer only (licence: NOASSERTION) · get_code("82a62f9681ee2957") |
| SAM_NonUniform_potential | Ran | baicenxiao/shaping-advice/maddpg/trainer/maddpg_spread.py pointer only (licence: NOASSERTION) · get_code("73da5c8faa128c71") |
| a_2_pa | Ran | baicenxiao/shaping-advice/maddpg/trainer/maddpg_spread.py pointer only (licence: NOASSERTION) · get_code("e9096a66724b29e3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Long-term temporal credit assignment is an important challenge in deep reinforcement learning (RL). It refers to the ability of the agent to attribute actions to consequences that may occur after a long time interval. Existing policy-gradient and Q-learning algorithms typically rely on dense environmental rewards that provide rich short-term supervision and help with credit assignment. However, they struggle to solve tasks with delays between an action and the corresponding rewarding feedback. To make credit assignment easier, recent works have proposed algorithms to learn dense guidance rewards that could be used in place of the sparse or delayed environmental rewards. This paper is in the same vein -starting with a surrogate RL objective that involves smoothing in the trajectory-space, we arrive at a new algorithm for learning guidance rewards. We show that the guidance rewards have an intuitive interpretation, and can be obtained without training any additional neural networks. Due to the ease of integration, we use the guidance rewards in a few popular algorithms (Q-learning, Actor-Critic, Distributional-RL) and present results in single-agent and multi-agent tasks that elucidate the benefit of our approach when the environmental rewards are sparse or delayed 1 .
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.12718")
get_code_for_paper("2010.12718")
have("2010.12718")
Connect an agent — have() is free.