/cog_tasks_rl_agents

Create cognitive tasks for Reinforcement Learning agents and benchmark them

Primary LanguagePythonMIT LicenseMIT

Cog_tasks_RL_agents

Create cognitive tasks for Reinforcement Learning agents and benchmark them

Background

Cognitive neuroscientists run a number of experiments in the lab to probe animal and human behaviour. But, machine learning / reinforcement learning (RL) researchers use very different benchmarks to evaluate their learning agents.To make it easier to compare the behavior of animals / humans with these agents, we need to implement the cognitive neuroscience tasks in environments that are accessible to artificial reinforcement learning agents.

What is known:

  • The performance of machine learning agent on machine learning task
  • The performance of cognitive agent on cognitive task

What is unknown:

  • The performance of machine learning agent on cognitive task
  • The performance of the cognitive agent on machine learningtask.

Agents

  • AuGMEnT
  • LSTM
  • DQN
  • HER
  • Monte Carlo

Tasks

Implemented in the OpenAI gym style. They are put in a independent repo here.

  • 1_2AX (custom)
  • 1_2AX_S (custom)
  • AX_CPT (custom)
  • Copy (gym)
  • Copy_repeat (gym)
  • Saccades (custom)
  • Seq_prediction

Benchmark

Every agent is trained and evaluated on each of the tasks.