Final Project of Reinforcement Learning Lecture (Roberto Capobianco, WS 2023/2024), Sapienza

Fryderyk Mantiuk, mail: mantiuk.2124851@studenti.uniroma1.it

Clara Grotehans, mail: grotehans.2121604@studenti.uniroma1.it

Implementating "Guiding Pretraining in Reinforcement Learning with Large Language Models" by Y. Du, O. Watkins, et. al.

• Link to Paper
• Link to Code Base

Project Guideline:

For an overview of this project, feel free to check out our presentation slides: Link to Google Slides

1. Installation (using conda)

$ conda env create -f text_crafter_ellm.yml

2. Default Config Variables:

• name: CrafterTextEnv-v1: Which gym environment is activated in init
• action_space_type: harder: Every combination of all possible verbs, e.g.: "make, drink, etc." + all possible objects "tree, bush, plant, zombie", which therefore also includes non-sensical actions like "drink tree"
• env_reward: False Only use intrinsic reward for ELLM algorithm
• seed: 1
• dying: True: Agent can die during episode if health status is too low
• length: 400: Maximum episode length
• similarity_threshold: 0.99: What threshold to use when comparing the performed action and suggested goals of LLM goal generator with a cosine similarity
• check_ac_success: False: i.e., when set to True, checks whether the agent targets the object of the action, which is always a combination of verb+object. E.g.: Agent targets tree, performed action is: "drink tree" --> Action would be considered as successfull, even though it is non-sensical. Therefore the agent would still be rewarded. We need this for Baselines where we do not use an LLM that should only suggest sensical and context-sensitive goals
• novelty_bias: False: Each episode: only reward the agent for an action that they haven't performed and have been rewarded for yet --> filter goals suggestions
• goal_generator: LLMGoalGenerator #Options: "LLMGoalGenerator", "ConstantGoalGenerator", "ConstantSamplerGoalGenerator": "LLMGoalGenerator" uses a LLM to suggest goals, For Baselines: "LLMGoalGenerator" suggests the whole action space as goals, "ConstantSamplerGoalGenerator" samples uniformly from the whole action space and suggests one goal each step
• language_model: mistral7binstruct #Options: "mistral7binstruct", "testllm" View Mistral Model on: Hugging Face
• frame_stack: 4 Agent sees the current and the last 3 frames each step

3. Start Training:

3.1. ELLM Training

3.1.a. With Novelty Bias

• Language Model: language_model: mistral7binstruct View Mistral Model on: Hugging Face
• Goal Generator: goal_generator: LLMGoalGenerator
• Novelty Bias: novelty_bias: True
• Action Check: check_ac_success: False - "False", because LLM should only suggest actions that are context-sensitive (because of prompting) and sensical (because of our common-sense hypothesis about LLMs).

$ python train.py \ # Uses configs/default_config.yaml for default train parameters

3.1.a. Without Novelty Bias

• Language Model: language_model: mistral7binstruct View Mistral Model on: Hugging Face
• Goal Generator: goal_generator: LLMGoalGenerator
• Novelty Bias: novelty_bias: False - Agent gets rewarded for actions he already performed previously
• Action Check: check_ac_success: False - "False", because LLM should only suggest actions that are context-sensitive (because of prompting) and sensical (because of our common-sense hypothesis about LLMs).

$ python train.py --novelty_bias False\ # Uses configs/default_config.yaml for default train parameters

3.2. Baseline Training: Novelty (context-sensitive, but potentially nonsensical actions are rewarded + Novelty bias)

• Language Model: language_model: None
• Goal Generator: goal_generator: ConstantGoalGenerator, which outputs the whole action space as suggested goals on each step
• Novelty Bias: novelty_bias: True
• Action Check: check_ac_success: True, such that only context-sensitive goals can be rewarded.

$ python train.py \ # Uses configs/default_config.yaml for default train parameters
    --goal_generator ConstantGoalGenerator\
    --novelty_bias True \
    --check_ac_success True

3.3. Baseline Training: Uniform (context-sensitive, but nonsensical actions are rewarded + Sampling from action space)

• Language Model: None
• Goal Generator: Constant Sampler Goal Generator, which outputs the action space as suggested goals on each step (goal_generator: ConstantSamplerGoalGenerator)
• Novelty Bias: novelty_bias: False
• Action Check: check_ac_success: False, such that only context sensitive goals can be rewarded.

$ python train.py \ # Uses configs/default_config.yaml for default train parameters
    --goal_generator ConstantSamplerGoalGenerator\
    --novelty_bias False \
    --check_ac_success True