We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| natashamjaques/neural_chat | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| load_pickle | Ran | natashamjaques/neural_chat/BatchRL/way_off_policy_batch_rl.py code served (permissive licence) · get_code("70db7c9cbe9e680d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment. These are critical shortcomings for applying RL to real-world problems where collecting data is expensive, and models must be tested offline before being deployed to interact with the environment -- e.g. systems that learn from human interaction. Thus, we develop a novel class of off-policy batch RL algorithms, which are able to effectively learn offline, without exploring, from a fixed batch of human interaction data. We leverage models pre-trained on data as a strong prior, and use KL-control to penalize divergence from this prior during RL training. We also use dropout-based uncertainty estimates to lower bound the target Q-values as a more efficient alternative to Double Q-Learning. The algorithms are tested on the problem of open-domain dialog generation -- a challenging reinforcement learning problem with a 20,000-dimensional action space. Using our Way Off-Policy algorithm, we can extract multiple different reward functions post-hoc from collected human interaction data, and learn effectively from all of these. We test the real-world generalization of these systems by deploying them live to converse with humans in an open-domain setting, and demonstrate that our algorithm achieves significant improvements over prior methods in off-policy batch RL.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1907.00456")
get_code_for_paper("1907.00456")
have("1907.00456")
Connect an agent — have() is free.