Introduction

In Gym-style reinforcement learning environments, reset() is required before the first step() of a new episode and again after an episode ends. The exact signal depends on the API version: older Gym used a single done flag, while newer Gymnasium-style APIs separate episode end into terminated and truncated.

reset() Starts a Fresh Episode

An environment episode begins with reset(). That call initializes the state and returns the first observation.

In the older Gym style:

obs = env.reset()

In newer Gymnasium-style APIs:

obs, info = env.reset()

Without reset(), the environment has no guarantee that it is in a valid starting state for a new episode.

Call reset() Again After the Episode Ends

In the classic API, an episode ends when done becomes True.

1obs = env.reset()
2done = False
3
4while not done:
5    action = env.action_space.sample()
6    obs, reward, done, info = env.step(action)
7
8obs = env.reset()

In the newer API, you reset when either terminated or truncated is true.

1obs, info = env.reset()
2terminated = False
3truncated = False
4
5while not (terminated or truncated):
6    action = env.action_space.sample()
7    obs, reward, terminated, truncated, info = env.step(action)
8
9obs, info = env.reset()

That is the core lifecycle rule.

Why reset() Matters

An RL environment is episodic. After a terminal state, the environment is no longer in a clean state for continued interaction unless it explicitly supports that transition. reset() gives you:

• a fresh initial observation
• episode boundary semantics
• reproducible rollout control when seeds are involved
• a clean place to collect episode statistics

Skipping reset() after an episode ends often produces invalid training logic, stale state usage, or outright API misuse.

Some Environments Truncate Without Natural Termination

A subtle but important case is time-limit truncation. An episode may end not because the agent solved or failed the task, but because the environment hit a step limit.

That is why newer APIs separate:

• 'terminated: task reached a natural terminal state'
• 'truncated: episode was cut short, often by a time limit'

From the environment-management perspective, both require a reset before starting the next episode.

Do Not Reset on Every Step

A common beginner mistake is calling reset() every loop iteration. That destroys episode continuity and prevents learning from multi-step consequences.

Wrong:

1for _ in range(100):
2    obs = env.reset()
3    action = env.action_space.sample()
4    obs, reward, done, info = env.step(action)

This repeatedly restarts the environment after one step. It is usually not what you want.

The correct pattern is one reset per episode, not one reset per action.

Seeding and Reproducibility

reset() is also often the place where seeding is applied or respected. If you need reproducible episodes, use the environment's seeding or reset API intentionally.

obs, info = env.reset(seed=42)

This does not mean every reset should reuse the same seed in a training loop. That would reduce episode variety. But it is useful for debugging or deterministic evaluation.

Common Pitfalls

• Forgetting to call reset() before the first step() leaves the environment state undefined for a new episode.
• Continuing to call step() after done, terminated, or truncated without a reset breaks the episode lifecycle.
• Resetting every step instead of every episode prevents meaningful temporal learning.
• Ignoring the difference between natural termination and time-limit truncation can confuse evaluation logic, even though both require a reset.
• Reusing the same seed on every reset during training can reduce state diversity and hurt learning.

Summary

• Call reset() before the first step of an episode.
• Call reset() again after the episode ends.
• In old Gym, that means after done == True.
• In newer APIs, that means after either terminated or truncated.
• Do not reset every step; reset once per episode.