Off-Policy Training¶
In online reinforcement learning, an agent is able to gather data by directly interacting with its environment. It can then use this experience to learn from and update its policy. To enable our agent to interact in this way, the agent needs to act either in the real world, or in a simulation.
AgileRL’s online training framework enables agents to learn in environments, using the standard Gym interface, 10x faster than SOTA by using our Evolutionary Hyperparameter Optimization algorithm.
Off-policy reinforcement learning involves decoupling the learning policy from the data collection policy. Algorithms like Q-learning and DDPG enable learning from experiences collected by a different, possibly exploratory policy, allowing for greater flexibility in exploration and improved sample efficiency. By learning from a diverse set of experiences, off-policy methods can leverage past data more effectively, separating the exploration strategy from the learning strategy and enabling the agent to learn optimal policies even from suboptimal or random exploration policies. This independence between data collection and learning policies often results in higher potential for reuse of previously gathered experiences and facilitates more efficient learning.
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Population Creation¶
To perform evolutionary HPO, we require a population of agents. Individuals in this population will share experiences but learn individually, allowing us to determine the efficacy of certain hyperparameters. Individual agents which learn best are more likely to survive until the next generation, and so their hyperparameters are more likely to remain present in the population. The sequence of evolution (tournament selection followed by mutation) is detailed further below.
from agilerl.utils.utils import makeVectEnvs, initialPopulation
import torch
NET_CONFIG = {
'arch': 'mlp', # Network architecture
'hidden_size': [32, 32], # Actor hidden size
}
INIT_HP = {
'DOUBLE': True, # Use double Q-learning
'BATCH_SIZE': 128, # Batch size
'LR': 1e-3, # Learning rate
'GAMMA': 0.99, # Discount factor
'LEARN_STEP': 1, # Learning frequency
'TAU': 1e-3, # For soft update of target network parameters
'CHANNELS_LAST': False # Swap image channels dimension from last to first [H, W, C] -> [C, H, W]
}
env = makeVectEnvs('LunarLander-v2', num_envs=16) # Create environment
try:
state_dim = env.single_observation_space.n # Discrete observation space
one_hot = True # Requires one-hot encoding
except Exception:
state_dim = env.single_observation_space.shape # Continuous observation space
one_hot = False # Does not require one-hot encoding
try:
action_dim = env.single_action_space.n # Discrete action space
except Exception:
action_dim = env.single_action_space.shape[0] # Continuous action space
if INIT_HP['CHANNELS_LAST']:
state_dim = (state_dim[2], state_dim[0], state_dim[1])
agent_pop = initialPopulation(algo='DQN', # Algorithm
state_dim=state_dim, # State dimension
action_dim=action_dim, # Action dimension
one_hot=one_hot, # One-hot encoding
net_config=NET_CONFIG, # Network configuration
INIT_HP=INIT_HP, # Initial hyperparameters
population_size=6, # Population size
device=torch.device("cuda"))
Experience Replay¶
In order to efficiently train a population of RL agents, off-policy algorithms must be used to share memory within populations. This reduces the exploration needed by an individual agent because it allows faster learning from the behaviour of other agents. For example, if you were able to watch a bunch of people attempt to solve a maze, you could learn from their mistakes and successes without necessarily having to explore the entire maze yourself.
The object used to store experiences collected by agents in the environment is called the Experience Replay Buffer, and is defined by the class ReplayBuffer().
During training it can be added to using the ReplayBuffer.save2memory() function, or ReplayBuffer.save2memoryVectEnvs() for vectorized environments (recommended).
To sample from the replay buffer, call ReplayBuffer.sample().
from agilerl.components.replay_buffer import ReplayBuffer
import torch
field_names = ["state", "action", "reward", "next_state", "done"]
memory = ReplayBuffer(action_dim=action_dim, # Number of agent actions
memory_size=10000, # Max replay buffer size
field_names=field_names, # Field names to store in memory
device=torch.device("cuda"))
Tournament Selection¶
Tournament selection is used to select the agents from a population which will make up the next generation of agents. If elitism is used, the best agent from a population is automatically preserved and becomes a member of the next generation. Then, for each tournament, k individuals are randomly chosen, and the agent with the best evaluation fitness is preserved. This is repeated until the population for the next generation is full.
The class TournamentSelection() defines the functions required for tournament selection. TournamentSelection.select() returns the best agent, and the new generation
of agents.
from agilerl.hpo.tournament import TournamentSelection
tournament = TournamentSelection(tournament_size=2, # Tournament selection size
elitism=True, # Elitism in tournament selection
population_size=6, # Population size
evo_step=1) # Evaluate using last N fitness scores
Mutation¶
Mutation is periodically used to explore the hyperparameter space, allowing different hyperparameter combinations to be trialled during training. If certain hyperparameters prove relatively beneficial to training, then that agent is more likely to be preserved in the next generation, and so those characteristics are more likely to remain in the population.
- The
Mutations()class is used to mutate agents with pre-set probabilities. The available mutations currently implemented are: No mutation
Network architecture mutation - adding layers or nodes. Trained weights are reused and new weights are initialized randomly.
Network parameters mutation - mutating weights with Gaussian noise.
Network activation layer mutation - change of activation layer.
RL algorithm mutation - mutation of learning hyperparameter, such as learning rate or batch size.
Mutations.mutation() returns a mutated population.
Tournament selection and mutation should be applied sequentially to fully evolve a population between evaluation and learning cycles.
from agilerl.hpo.mutation import Mutations
import torch
mutations = Mutations(algo='DQN', # Algorithm
no_mutation=0.4, # No mutation
architecture=0.2, # Architecture mutation
new_layer_prob=0.2, # New layer mutation
parameters=0.2, # Network parameters mutation
activation=0, # Activation layer mutation
rl_hp=0.2, # Learning HP mutation
rl_hp_selection=['lr', 'batch_size'], # Learning HPs to choose from
mutation_sd=0.1, # Mutation strength
arch=NET_CONFIG['arch'], # Network architecture
rand_seed=1, # Random seed
device=torch.device("cuda"))
Off-policy Training Loop¶
Now it is time to insert the evolutionary HPO components into our training loop. If you are using a Gym-style environment, it is easiest to use our training function, which returns a population of trained agents and logged training metrics.
from agilerl.training.train_off_policy import train_off_policy
import gymnasium as gym
import torch
trained_pop, pop_fitnesses = train_off_policy(env=env, # Gym-style environment
env_name='LunarLander-v2', # Environment name
algo='DQN', # Algorithm
pop=agent_pop, # Population of agents
memory=memory, # Replay buffer
swap_channels=INIT_HP['CHANNELS_LAST'], # Swap image channel from last to first
n_episodes=1000, # Max number of training episodes
evo_epochs=20, # Evolution frequency
evo_loop=1, # Number of evaluation episodes per agent
target=200., # Target score for early stopping
tournament=tournament, # Tournament selection object
mutation=mutations, # Mutations object
wb=False) # Weights and Biases tracking
Alternatively, use a custom training loop. Combining all of the above:
from agilerl.utils.utils import makeVectEnvs, initialPopulation
from agilerl.components.replay_buffer import ReplayBuffer
from agilerl.hpo.tournament import TournamentSelection
from agilerl.hpo.mutation import Mutations
import gymnasium as gym
import numpy as np
import torch
NET_CONFIG = {
'arch': 'mlp', # Network architecture
'hidden_size': [32, 32], # Actor hidden size
}
INIT_HP = {
'DOUBLE': True, # Use double Q-learning
'BATCH_SIZE': 128, # Batch size
'LR': 1e-3, # Learning rate
'GAMMA': 0.99, # Discount factor
'LEARN_STEP': 1, # Learning frequency
'TAU': 1e-3, # For soft update of target network parameters
'CHANNELS_LAST': False # Swap image channels dimension from last to first [H, W, C] -> [C, H, W]
}
env = makeVectEnvs('LunarLander-v2', num_envs=16) # Create environment
try:
state_dim = env.single_observation_space.n # Discrete observation space
one_hot = True # Requires one-hot encoding
except Exception:
state_dim = env.single_observation_space.shape # Continuous observation space
one_hot = False # Does not require one-hot encoding
try:
action_dim = env.single_action_space.n # Discrete action space
except Exception:
action_dim = env.single_action_space.shape[0] # Continuous action space
if INIT_HP['CHANNELS_LAST']:
state_dim = (state_dim[2], state_dim[0], state_dim[1])
pop = initialPopulation(algo='DQN', # Algorithm
state_dim=state_dim, # State dimension
action_dim=action_dim, # Action dimension
one_hot=one_hot, # One-hot encoding
net_config=NET_CONFIG, # Network configuration
INIT_HP=INIT_HP, # Initial hyperparameters
population_size=6, # Population size
device=torch.device("cuda"))
field_names = ["state", "action", "reward", "next_state", "done"]
memory = ReplayBuffer(action_dim=action_dim, # Number of agent actions
memory_size=10000, # Max replay buffer size
field_names=field_names, # Field names to store in memory
device=torch.device("cuda"))
tournament = TournamentSelection(tournament_size=2, # Tournament selection size
elitism=True, # Elitism in tournament selection
population_size=6, # Population size
evo_step=1) # Evaluate using last N fitness scores
mutations = Mutations(algo='DQN', # Algorithm
no_mutation=0.4, # No mutation
architecture=0.2, # Architecture mutation
new_layer_prob=0.2, # New layer mutation
parameters=0.2, # Network parameters mutation
activation=0, # Activation layer mutation
rl_hp=0.2, # Learning HP mutation
rl_hp_selection=['lr', 'batch_size'], # Learning HPs to choose from
mutation_sd=0.1, # Mutation strength
arch=NET_CONFIG['arch'], # Network architecture
rand_seed=1, # Random seed
device=torch.device("cuda"))
max_episodes = 1000 # Max training episodes
max_steps = 500 # Max steps per episode
# Exploration params
eps_start = 1.0 # Max exploration
eps_end = 0.1 # Min exploration
eps_decay = 0.995 # Decay per episode
epsilon = eps_start
evo_epochs = 5 # Evolution frequency
evo_loop = 1 # Number of evaluation episodes
# TRAINING LOOP
for idx_epi in range(max_episodes):
for agent in pop: # Loop through population
state = env.reset()[0] # Reset environment at start of episode
score = 0
for idx_step in range(max_steps):
if INIT_HP['CHANNELS_LAST']:
state = np.moveaxis(state, [3], [1])
action = agent.getAction(state, epsilon) # Get next action from agent
next_state, reward, done, _, _ = env.step(action) # Act in environment
# Save experience to replay buffer
if INIT_HP['CHANNELS_LAST']:
memory.save2memoryVectEnvs(
state, action, reward, np.moveaxis(next_state, [3], [1]), done)
else:
memory.save2memoryVectEnvs(
state, action, reward, next_state, done)
# Learn according to learning frequency
if memory.counter % agent.learn_step == 0 and len(memory) >= agent.batch_size:
experiences = memory.sample(agent.batch_size) # Sample replay buffer
agent.learn(experiences) # Learn according to agent's RL algorithm
state = next_state
score += reward
epsilon = max(eps_end, epsilon*eps_decay) # Update epsilon for exploration
# Now evolve population if necessary
if (idx_epi+1) % evo_epochs == 0:
# Evaluate population
fitnesses = [agent.test(env, swap_channels=False, max_steps=max_steps, loop=evo_loop) for agent in pop]
print(f'Episode {idx_epi+1}/{max_episodes}')
print(f'Fitnesses: {["%.2f"%fitness for fitness in fitnesses]}')
print(f'100 fitness avgs: {["%.2f"%np.mean(agent.fitness[-100:]) for agent in pop]}')
# Tournament selection and population mutation
elite, pop = tournament.select(pop)
pop = mutations.mutation(pop)
env.close()