docs/quickstart.md

Creating and Stepping Environments

Our tasks are gymnax environments and work with wrappers and code designed to work with gymnax. The following example demonstrates how to integrate POPGym Arcade into your code.

import popgym_arcade
import jax

# Create POMDP env variant
env, env_params = popgym_arcade.make("BattleShipEasy", partial_obs=True)

# Let's vectorize and compile the env
# Note when you are training a policy, it is better to compile your policy_update rather than the env_step
reset = jax.jit(jax.vmap(env.reset, in_axes=(0, None)))
step = jax.jit(jax.vmap(env.step, in_axes=(0, 0, 0, None)))
    
# Initialize four vectorized environments
n_envs = 4
# Initialize PRNG keys
key = jax.random.key(0)
reset_keys = jax.random.split(key, n_envs)
    
# Reset environments
observation, env_state = reset(reset_keys, env_params)

# Step the POMDP
for t in range(10):
    # Propagate some randomness
    action_key, step_key = jax.random.split(jax.random.key(t))
    action_keys = jax.random.split(action_key, n_envs)
    step_keys = jax.random.split(step_key, n_envs)
    # Pick actions at random
    actions = jax.vmap(env.action_space(env_params).sample)(action_keys)
    # Step the env to the next state
    # No need to reset after initial reset, gymnax automatically resets when done
    observation, env_state, reward, done, info = step(step_keys, env_state, actions, env_params)

# POMDP and MDP variants share states
# We can plug the POMDP states into the MDP and continue playing
mdp, mdp_params = popgym_arcade.make("BattleShipEasy", partial_obs=False)
mdp_reset = jax.jit(jax.vmap(mdp.reset, in_axes=(0, None)))
mdp_step = jax.jit(jax.vmap(mdp.step, in_axes=(0, 0, 0, None)))

action_keys = jax.random.split(jax.random.key(t + 1), n_envs)
step_keys = jax.random.split(jax.random.key(t + 2), n_envs)
markov_state, env_state, reward, done, info = mdp_step(step_keys, env_state, actions, mdp_params)