Unified Model-Free Hierarchical Reinforcement Learning Framework
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
montezuma-AAAI
montezuma-ICLR
rooms-ICLR
.gitignore
LICENSE
README.md
_config.yml

README.md

unified-hrl

Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale applications involving huge state spaces and sparse delayed reward feedback. Hierarchical Reinforcement Learning (HRL) methods attempt to address this scalability issue by learning action selection policies at multiple levels of temporal abstraction. Abstraction can be had by identifying a relatively small set of states that are likely to be useful as subgoals, in concert with the learning of corresponding skill policies to achieve those subgoals. Many approaches to subgoal discovery in HRL depend on the analysis of a model of the environment, but the need to learn such a model introduces its own problems of scale. Once subgoals are identified, skills may be learned through intrinsic motivation, introducing an internal reward signal marking subgoal attainment. This is a repository for a paper presented in AAAI 2019, Knowledge Extraction from Games Workshop. We present a novel modelfree method for subgoal discovery using incremental unsupervised learning over a small memory of the most recent experiences (trajectories) of the agent. When combined with an intrinsic motivation learning mechanism, this method learns both subgoals and skills, based on experiences in the environment. Thus, we offer an original approach to HRL that does not require the acquisition of a model of the environment, suitable for large-scale applications. We demonstrate the efficiency of our method on two RL problems with sparse delayed feedback: a variant of the rooms environment and the first screen of the ATARI 2600 Montezuma’s Revenge game.

Code

The code is in PyTorch. It includes unsupervised subgoal discovery, temporal abstraction, and intrinsic motivation learning. For two tasks: Navigation in Rooms Task nad first room of Montezuma's Revenge.

python main.py