GitHub / Rahgooy / soft_constraint_irl
In this project, we use the maximum entropy principle in Inverse reinforcement learning to learn soft constraints from demonstrations obtained from an agent interacting with a non-deterministic MDP. In the second part of this project, we implement various strategies (orchestrators) to mix conflicting policies (e.g. pragmatic vs ethical). In one of these orchestrators, we use a cognitive model of decision making (MDFT) to enable the agent to make human-like decisions.
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PURL: pkg:github/Rahgooy/soft_constraint_irl
Stars: 1
Forks: 0
Open issues: 2
License: mit
Language: Python
Size: 212 MB
Dependencies parsed at: Pending
Created at: over 4 years ago
Updated at: almost 2 years ago
Pushed at: over 3 years ago
Last synced at: almost 2 years ago
Topics: cognitive-models, gradient-descent, mdft, reinforcement-learning