Reinforcement-Learning Environments

This module provides a small, proof-friendly environment interface inspired by Gym/Gymnasium:

• reset returns an initial observation and state,
• stepGym returns (observation, reward, terminated, truncated, state),
• helper functions build array-backed state traces and transition rollouts from an action sequence.

Unlike a typical Python RL environment, this interface is purely functional and keeps the hidden state explicit. That makes it much easier to state and prove safety / invariant properties.

References:

• Gymnasium API reference (reset/step, terminated vs truncated): https://gymnasium.farama.org/
• TorchRL documentation (environment transforms, rollout collection, and TensorDict interface): https://pytorch.org/rl/
• Sutton and Barto, Reinforcement Learning: An Introduction (2nd ed.): http://incompleteideas.net/book/the-book-2nd.html

structure Spec.RL.StepResult (State : Type u) (Reward : Type v) : Type (max u v)

Result of stepping an environment from one state with one action.

• state : State

  Next latent state.

• reward : Reward

  Immediate reward.

• terminated : Bool

  Task-defined terminal flag.

• truncated : Bool

  Time-limit / truncation flag.

def Spec.RL.StepResult.done {State : Type u} {Reward : Type z} (r : StepResult State Reward) : Bool

Gymnasium-style done flag: an episode is done if it is terminated or truncated.

structure Spec.RL.ObservedTransition (Observation : Type u) (Action : Type v) (Reward : Type w) : Type (max (max u v) w)

Rollout record that stores observations on both sides of a step.

• observation : Observation

  Observation before the action.

• action : Action

  Action taken.

• reward : Reward

  Reward returned by the environment.

• nextObservation : Observation

  Observation after the step.

• terminated : Bool

  Task-defined terminal flag.

• truncated : Bool

  Time-limit / truncation flag.

structure Spec.RL.Env (State : Type u) (Action : Type v) (Observation : Type w) (Reward : Type z) : Type (max (max (max u v) w) z)

Pure Gym-style environment with explicit latent state.

• initialState : State

  Initial latent state used by reset.

• observe : State → Observation

  Observation function from latent states.

• step : State → Action → StepResult State Reward

  Single-step transition function.

def Spec.RL.reset {State : Type u} {Action : Type v} {Observation : Type w} {Reward : Type z} (env : Env State Action Observation Reward) : Observation × State

Gym-style reset: return the initial observation and latent state.

def Spec.RL.stepGym {State : Type u} {Action : Type v} {Observation : Type w} {Reward : Type z} (env : Env State Action Observation Reward) (state : State) (action : Action) : Observation × Reward × Bool × Bool × State

Gym-style step result: (nextObservation, reward, terminated, truncated, nextState).

def Spec.RL.stateAfter {State : Type u} {Action : Type v} {Observation : Type w} {Reward : Type z} (env : Env State Action Observation Reward) (initialState : State) (actions : Array Action) : Nat → State

State reached after the first n actions, or after every available action when n is larger.

def Spec.RL.evolveFrom {State : Type u} {Action : Type v} {Observation : Type w} {Reward : Type z} (env : Env State Action Observation Reward) (state : State) (actions : Array Action) : State

Final latent state reached after executing an array of actions.

def Spec.RL.evolve {State : Type u} {Action : Type v} {Observation : Type w} {Reward : Type z} (env : Env State Action Observation Reward) (actions : Array Action) : State

Final latent state reached from the environment's initial state.

def Spec.RL.statesFrom {State : Type u} {Action : Type v} {Observation : Type w} {Reward : Type z} (env : Env State Action Observation Reward) (state : State) (actions : Array Action) : Array State

State trace that records the initial state and every successor state.

def Spec.RL.states {State : Type u} {Action : Type v} {Observation : Type w} {Reward : Type z} (env : Env State Action Observation Reward) (actions : Array Action) : Array State

State trace from the environment's initial state.

def Spec.RL.rolloutFrom {State : Type u} {Action : Type v} {Observation : Type w} {Reward : Type z} (env : Env State Action Observation Reward) (state : State) (actions : Array Action) : Array (ObservedTransition Observation Action Reward)

Observed transition rollout from an explicit initial state.

def Spec.RL.rollout {State : Type u} {Action : Type v} {Observation : Type w} {Reward : Type z} (env : Env State Action Observation Reward) (actions : Array Action) : Array (ObservedTransition Observation Action Reward)

Observed transition rollout from the environment's initial state.

structure Spec.RL.SafeEnv (State : Type u) (Action : Type v) (Observation : Type w) (Reward : Type z) : Type (max (max (max u v) w) z)

Environment with an invariant and an action-validity predicate.

• toEnv : Env State Action Observation Reward

  Underlying environment dynamics.

• Invariant : State → Prop

  State invariant we want preserved along valid executions.

• ActionOk : State → Action → Prop

  Legality / admissibility of actions at a state.

• init_safe : self.Invariant self.toEnv.initialState

  Reset starts in a safe state.

• step_safe {state : State} {action : Action} : self.Invariant state → self.ActionOk state action → self.Invariant (self.toEnv.step state action).state

  One valid step preserves the invariant.

def Spec.RL.SafeEnv.actionPathOk {State : Type u} {Action : Type v} {Observation : Type w} {Reward : Type z} (env : SafeEnv State Action Observation Reward) (state : State) (actions : Array Action) : Prop

Every action is valid in the state reached immediately before it is executed.