Continual Learning Module

This module provides popular continual learning baseline implementations on top of the Soft-Actor-Critic (SAC) algorithm. The implementation is based on Tensorflow.

Installation

To install the continual learning module, run the following command:

$ pip install COOM[cl]

Running Experiments

You can run single task or continual learning experiments with CL/run_single.py and CL/run_cl.py scripts, respectively. To see available script arguments, run with --help option, e.g. python CL/run_single.py --help

Single task

python CL/run_single.py --scenario pitfall

Continual learning

python CL/run_cl.py --sequence CO4 --cl_method packnet

Reproducing Experimental Results

We have also listed all the commands for running the experiments in our paper in cl.sh and single.sh. We used seeds [0, 1, ..., 9] for all experiments in the paper.

Average Performance, Forgetting, Forward Transfer and Action Distributions

We evaluate the continual learning methods on the COOM benchmark based on Average Performance, Forgetting, and Forward Transfer. We use the following CL methods:

python CL/run_cl.py --sequence [SEQUENCE] --seed [SEED] --cl_method packnet --packnet_retrain_steps 10000 --clipnorm 2e-05
python CL/run_cl.py --sequence [SEQUENCE] --seed [SEED] --cl_method mas --cl_reg_coef=10000
python CL/run_cl.py --sequence [SEQUENCE] --seed [SEED] --cl_method agem --regularize_critic --episodic_mem_per_task 10000 --episodic_batch_size 128
python CL/run_cl.py --sequence [SEQUENCE] --seed [SEED] --cl_method l2 --cl_reg_coef=100000
python CL/run_cl.py --sequence [SEQUENCE] --seed [SEED] --cl_method ewc --cl_reg_coef=250
python CL/run_cl.py --sequence [SEQUENCE] --seed [SEED] --cl_method vcl --cl_reg_coef=1 --vcl_first_task_kl False
python CL/run_cl.py --sequence [SEQUENCE] --seed [SEED] --cl_method clonex --exploration_kind 'best_return' --cl_reg_coef=100 --episodic_mem_per_task 10000 --episodic_batch_size 128
python CL/run_cl.py --sequence [SEQUENCE] --seed [SEED] --batch_size 512 --buffer_type reservoir --reset_buffer_on_task_change False --replay_size 8e5  # Perfect Memory
python CL/run_cl.py --sequence [SEQUENCE] --seed [SEED]  # Fine-tuning

We ran the COC sequence with sparse reward and only with PackNet:

python CL/run_cl.py --sequence COC --seed [SEED] --sparse_rewards --cl_method packnet --packnet_retrain_steps 10000 --clipnorm 2e-05

Measuring Forward Transfer also requires running SAC on each task in isolation:

python CL/run_single.py --scenario [SCENARIO] --envs [ENVS] --seed [SEED] --no_test

Network plasticity

To reproduce our network plasticity experiments from the paper, run the following command:

python CL/run_cl.py --sequence CO8 --seed [SEED] --repeat_sequence 10 --no_test --steps_per_env 100000

Method Variations

To reproduce our method variations experiments from the paper, run the following command:

Image Augmentations

1. Random Convolution
2. Random Shift
3. Random Noise

python CL/run_cl.py --sequence CO8 --cl_method [METHOD] --seed [SEED] --augment --augmentation conv
python CL/run_cl.py --sequence CO8 --cl_method [METHOD] --seed [SEED] --augment --augmentation shift
python CL/run_cl.py --sequence CO8 --cl_method [METHOD] --seed [SEED] --augment --augmentation noise

Prioritized Experience Replay (PER)

python CL/run_cl.py --sequence CO8 --cl_method [METHOD] --seed [SEED] --buffer_type prioritized

LSTM

python CL/run_cl.py --sequence CO8 --cl_method [METHOD] --seed [SEED] --use_lstm

Critic Regularization

python CL/run_cl.py --sequence CO8 --cl_method [METHOD] --seed [SEED] --regularize_critic

Command Line Arguments

Below is a table of the available command line arguments for the script:

Category	Argument	Default	Description
Core	--scenarios	None	Scenarios to run. Choices: health_gathering, run_and_gun, dodge_projectiles, chainsaw, raise_the_roof, floor_is_lava, hide_and_seek, arms_dealer, pitfall
	--envs	['default']	Name of the environments in the scenario(s) to run
	--test_envs	[]	Name of the environments to periodically evaluate the agent on
	--no_test	False	If True, no test environments will be used
	--seed	0	Seed for randomness
	--gpu	None	Which GPU to use
	--sparse_rewards	False	Whether to use the sparse reward setting
Continual Learning	--sequence	None	Name of the continual learning sequence. Choices: CD4, CD8, CD16, CO4, CO8, CO16, COC, MIXED
	--cl_method	None	Continual learning method. Choices: clonex, owl, l2, ewc, mas, vcl, packnet, agem
	--start_from	0	Which task to start/continue the training from
	--num_repeats	1	How many times to repeat the sequence
	--random_order	False	Whether to randomize the order of the tasks
DOOM	--render	False	Render the environment
	--render_sleep	0.0	Sleep time between frames when rendering
	--variable_queue_length	5	Number of game variables to remember
	--frame_skip	4	Number of frames to skip
	--resolution	None	Screen resolution of the game. Choices: 800X600, 640X480, 320X240, 160X120
Save/Load	--save_freq_epochs	25	Save the model parameters after n epochs
	--model_path	None	Path to load the model from
Recording	--record	False	Whether to record gameplay videos
	--record_every	100	Record gameplay video every n episodes
	--video_folder	'videos'	Path to save the gameplay videos
Logging	--with_wandb	False	Enables Weights and Biases
	--logger_output	["tsv", "tensorboard"]	Types of logger used. Choices: neptune, tensorboard, tsv
	--group_id	"default_group"	Group ID, for grouping logs from different experiments into common directory
	--log_every	1000	Number of steps between subsequent evaluations and logging
Model	--use_lstm	False	Whether to use an LSTM after the CNN encoder head
	--hidden_sizes	[256, 256]	Hidden sizes list for the MLP models
	--activation	"lrelu"	Activation kind for the models
	--use_layer_norm	True	Whether to use layer normalization
	--multihead_archs	True	Whether to use multi-head architecture
	--hide_task_id	False	Whether the model knows the task during test time
Learning Rate	--lr	1e-3	Learning rate for the optimizer
	--lr_decay	'linear'	Method to decay the learning rate over time. Choices: None, 'linear', 'exponential'
	--lr_decay_rate	0.1	Rate to decay the learning
	--lr_decay_steps	1e5	Number of steps to decay the learning rate
Replay Buffer	--replay_size	5e4	Size of the replay buffer
	--buffer_type	"fifo"	Strategy of inserting examples into the buffer. Choices: fifo, other values as per BufferType enum
	--episodic_memory_from_buffer	True	[Description]
Training	--steps_per_env	2e5	Number of steps the algorithm will run per environment
	--update_after	5000	Number of env interactions to collect before starting to do update the gradient
	--update_every	500	Number of env interactions to do between every update
	--n_updates	50	Number of consecutive policy gradient descent updates to perform
	--batch_size	128	Minibatch size for the optimization
	--gamma	0.99	Discount factor
	--alpha	"auto"	Entropy regularization coefficient
	--target_output_std	0.089	Target standard deviation of the action distribution for dynamic alpha tuning
	--regularize_critic	False	Whether to regularize both actor and critic, or only actor
	--clipnorm	None	Value for gradient clipping
Testing	--test	True	Whether to test the model
	--test_only	False	Whether to only test the model
	--test_episodes	3	Number of episodes to test the model
Exploration	--start_steps	10000	Number of steps for uniform-random action selection
	--agent_policy_exploration	False	Whether to use uniform exploration only in the first task
	--exploration_kind	None	Kind of exploration to use at the beginning of a new task
Task Change	--reset_buffer_on_task_change	True	Whether to reset the replay buffer on task change
	--reset_optimizer_on_task_change	True	Whether to reset the optimizer on task change
	--reset_critic_on_task_change	False	Whether to reset the critic on task change
CL Method Specific	--packnet_retrain_steps	0	Number of retrain steps after network pruning per task
	--cl_reg_coef	0.0	Regularization strength for certain CL methods
	--vcl_first_task_kl	False	Use KL regularization for the first task in VCL
	--episodic_mem_per_task	0	Number of examples to keep in memory per task for AGEM
	--episodic_batch_size	0	Minibatch size for additional loss computation in AGEM
Observation	--frame_stack	4	Number of frames to stack
	--frame_height	84	Height of the frame
	--frame_width	84	Width of the frame
	--augment	False	Whether to use image augmentation
	--augmentation	None	Type of image augmentation. Choices: 'conv', 'shift', 'noise'
Reward	--reward_frame_survived	0.01	Reward for surviving a frame
	--reward_switch_pressed	15.0	Reward for pressing a switch
	--reward_kill_dtc	1.0	Reward for eliminating an enemy
	--reward_kill_rag	5.0	Reward for eliminating an enemy
	--reward_kill_chain	5.0	Reward for eliminating an enemy
	--reward_health_hg	15.0	Reward for picking up a health kit
	--reward_health_has	5.0	Reward for picking a health kit
	--reward_weapon_ad	15.0	Reward for picking a weapon
	--reward_delivery	30.0	Reward for delivering an item
	--reward_platform_reached	1.0	Reward for reaching a platform
	--reward_on_platform	0.1	Reward for staying on a platform
	--reward_scaler_pitfall	0.1	Reward scaler for traversal
	--reward_scaler_traversal	1e-3	Reward scaler for traversal
Penalty	--penalty_passivity	-0.1	Penalty for not moving
	--penalty_death	-1.0	Negative reward for dying
	--penalty_projectile	-0.01	Negative reward for projectile hit
	--penalty_health_hg	-0.01	Negative reward for losing health
	--penalty_health_dtc	-1.0	Negative reward for losing health
	--penalty_health_has	-5.0	Negative reward for losing health
	--penalty_lava	-0.1	Penalty for stepping on lava
	--penalty_ammo_used	-0.1	Negative reward for using ammo