Reinforcement learning agent that actively learns physical motion mechanisms in a 2D simulated domain. Sample videos of trained agent episodes are accessable in ./videos/.

Previous research paper: https://psyarxiv.com/6vr4g/

To train an active learning agent with q-value function approximator in separate framework, simply execute the following command with dependencies ready:

$ python learning_system.py --save_model True > exp_log/active_training_log.txt

or use active_learning_bash.sh to do the same thing.

• master & loss_reward: Separate training framework. Train active agent & predictor separately, reward agent for predictor's mean evaluation loss over 5 frames during each action (5 frames). Then generate new training data for model predicor with trained active agent.

• loss_reduction: Concurrent training framework. Train active agent & predictor together, reward agent for predictor's mean of training loss reduction over 5 frames during each action (5 frames).

Model predictor was pretrained on human experiment data with weighted average loss and learning rate = 1e-05 for 20 epochs.

Active agent was pretrained with reward only for catching & approaching pucks for 10000 episodes.

python 3.7+

tensorflow-gpu 1.13.1+

pyduktape 0.0.6

• learning_system.py An integreated launch script that runs separate training framework in loop. active_learning_bash.sh does the same thing but run scripts one by one.

• RQN_agent.py The active agent based on Recurrent Q-Network, includes training and testing methods.

• random_agent.py Baseline agent, samples random actions.

• interaction_env.py The interaction enviroment, defines action space and reward signals, communicate with JavaScript simulator.

• config.py Constant parameters define enviroment and model settings.

• js_simulator/ JavaScript simualtor, contains data generation and environment settings.

• model_predictor/ The world predictor based on LSTM, includes training and testing methods.

• model_predictor/video_generation.py Use moviepy.editor to generate videos with recorded trials data.

• exp_log/ & model_predictor/exp_log/ Training and testing logs of the active agent and world predictor

• checkpoints/ & model_predictor/checkpoints/ Checkpoints of pre-trained active agent and world predictor