Evaluating Memory and Credit Assignment in Memory-Based RL

This is the official code for the paper (Section 5.1 & 5.2: discrete control)

"When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment", NeurIPS 2023 (oral)

by Tianwei Ni, Michel Ma, Benjamin Eysenbach, and Pierre-Luc Bacon.

Please switch to the branch to check the code for Section 5.3 (PyBullet continuous control).

Modular Design

The code has a modular design which requires three configuration files. We hope that such design could facilitate future research on different environments, RL algorithms, and sequence models.

• config_env: specify the environment, with config_env.env_name specifying the exact (memory / credit assignment) length of the task
  • Passive T-Maze (this work)
  • Active T-Maze (this work)
  • Passive Visual Match (based on [Hung et al., 2018])
  • Key-to-Door (based on [Raposo et al., 2021])
• config_rl: specify the RL algorithm and its hyperparameters
  • DQN (with epsilon greedy)
  • SAC-Discrete (we find --freeze_critic can prevent gradient explosion, see the discussion in Appendix C.1 in the latest version of the arXiv paper).
• config_seq: specify the sequence model and its hyperparameters including training sequence length config_seq.sampled_seq_len and number of layers --config_seq.model.seq_model_config.n_layer
  • LSTM [Hochreiter and Schmidhuber, 1997]
  • Transformer (GPT-2) [Radford et al., 2019]

Installation

We use python 3.7+ and list the basic requirements in requirements.txt.

Reproducing the Results

Below are example commands to reproduce the main results shown in Figure 3 and 6. For the ablation results, please adjust the corresponding hyperparameters.

To run Passive T-Maze with a memory length of 50 with LSTM-based agent:

python main.py \
    --config_env configs/envs/tmaze_passive.py \
    --config_env.env_name 50 \
    --config_rl configs/rl/dqn_default.py \
    --train_episodes 20000 \
    --config_seq configs/seq_models/lstm_default.py \
    --config_seq.sampled_seq_len -1 \

To run Passive T-Maze with a memory length of 1500 with Transformer-based agent:

python main.py \
    --config_env configs/envs/tmaze_passive.py \
    --config_env.env_name 1500 \
    --config_rl configs/rl/dqn_default.py \
    --train_episodes 6700 \
    --config_seq configs/seq_models/gpt_default.py \
    --config_seq.sampled_seq_len -1 \

To run Active T-Maze with a memory length of 20 with Transformer-based agent:

python main.py \
    --config_env configs/envs/tmaze_active.py \
    --config_env.env_name 20 \
    --config_rl configs/rl/dqn_default.py \
    --train_episodes 40000 \
    --config_seq configs/seq_models/gpt_default.py \
    --config_seq.sampled_seq_len -1 \
    --config_seq.model.seq_model_config.n_layer 2 \
    --config_seq.model.seq_model_config.n_head 2 \

To run Passive Visual Match with a memory length of 60 with Transformer-based agent:

python main.py \
    --config_env configs/envs/visual_match.py \
    --config_env.env_name 60 \
    --config_rl configs/rl/sacd_default.py \
    --shared_encoder --freeze_critic \
    --train_episodes 40000 \
    --config_seq configs/seq_models/gpt_cnn.py \
    --config_seq.sampled_seq_len -1 \

To run Key-to-Door with a memory length of 120 with LSTM-based agent:

python main.py \
    --config_env configs/envs/keytodoor.py \
    --config_env.env_name 120 \
    --config_rl configs/rl/sacd_default.py \
    --shared_encoder --freeze_critic \
    --train_episodes 40000 \
    --config_seq configs/seq_models/lstm_cnn.py \
    --config_seq.sampled_seq_len -1 \
    --config_seq.model.seq_model_config.n_layer 2 \

To run Key-to-Door with a memory length of 250 with Transformer-based agent:

python main.py \
    --config_env configs/envs/visual_match.py \
    --config_env.env_name 250 \
    --config_rl configs/rl/sacd_default.py \
    --shared_encoder --freeze_critic \
    --train_episodes 30000 \
    --config_seq configs/seq_models/gpt_cnn.py \
    --config_seq.sampled_seq_len -1 \
    --config_seq.model.seq_model_config.n_layer 2 \
    --config_seq.model.seq_model_config.n_head 2 \

The train_episodes of each task is specified in budget.py.

By default, the logging data will be stored in logs/ folder with csv format. If you use --debug flag, it will be stored in debug/ folder.

Logging and Plotting

After the logging data is stored, you can plot the learning curves and aggregation plots (e.g., Figure 3 and 6) using vis.ipynb jupyter notebook.

We also provide our logging data used in the paper shared in google drive (< 400 MB).

Acknowledgement

The code is largely based on prior works:

• POMDP Baselines
• Hugging Face Transformers

Questions

If you have any questions, please raise an issue (preferred) or send an email to Tianwei ([email protected]).